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Aquest projecte, elaborat a l'inLab FIB UPC, aporta una nova solució al marc de la navegació per espais d'interior basada en la predicció del moviment de l'usuari a partir de les lectures de la unitat inercial d'un telèfon intel·ligent, és a dir, eines com l'acceleròmetre o el giroscopi. Juntament amb l'Autoritat del Transport Metropolità (ATM), s'ha aconseguit un sistema que permet guiar amb precisió als usuaris per les seves instal·lacions sense necessitat de geolocalització. No només això, sinó que el sistema resultant és modular i es pot fer servir com a mòdul de detecció de moviment a qualsevol aplicació mòbil. El sistema, basat en un model d'Intel·ligència Artificial, pot donar suport a tota mena de moviments, des de caminar a Persones amb Mobilitat Reduïda en cadira de rodes, si se l'entrena en el tipus de moviment corresponent. A més a més, és prou lleuger i eficient per poder executar-se fàcilment en un dispositiu mòbil, mostrant a l'usuari el seu moviment en temps real. Competeix amb sistemes similars desenvolupats per empreses del sector de la navegació per una fracció del cost i els recursos. A més d'això té l'objectiu de fer-se públic, permetent a altres desenvolupadors entrenar els seus propis models i poder integrar-ho amb les seves aplicacions., This project, developed at inLab FIB UPC, provides a new solution for indoor navigation based on predicting user movement from the readings of a smartphone's inertial unit, such as accelerometers or gyroscopes. In collaboration with the Autoritat del Transport Metropolità (ATM), a system has been achieved that allows users to be guided accurately through their facilities without the need for geolocation. Not only that, but the resulting system is modular and can be used as a motion detection module in any mobile application. The system, based on an Artificial Intelligence model, can support all kinds of movements, from walking to assisting Persons with Reduced Mobility in wheelchairs, if trained on the corresponding type of movement. Furthermore, it is lightweight and efficient enough to run easily on a mobile device, displaying the user's movement in real-time. It competes with similar systems developed by companies in the navigation sector at a fraction of the cost and resources. Additionally, it aims to be made public, allowing other developers to train their own models and integrate it into their applications.

Producción Científica, An end-to-end hardware-software pipeline is introduced to automatize ergonomics assessment in industrial workplaces. The proposed modular solution can interoperate with commercial systems throughout the ergonomics assessment phases involved in the process. The pipeline includes custom-designed Inertial Measurement Unit (IMU) sensors, two real-time worker movement acquisition tools, inverse kinematics processing and Rapid Upper Limb Assessment (RULA) report generation. It is based on free tools such as Unity3D and OpenSim to avoid the problems derived from using proprietary technologies, such as security decisions being made under “black box” conditions. Experiments were conducted in an automotive factory in a workplace with WMSDs risk among workers. The proposed solution obtained comparable results to a gold standard solution, reaching measured joint angles a 0.95 cross-correlation and a Root Mean Square Error (RMSE) lower than 10 for elbows and 12 for shoulders between both systems. In addition, the global RULA score difference is lower than 5 % between both systems. This work provides a low-cost solution for WMSDs risk assessment in the workplace to reduce musculoskeletal disorders and associated sick leave in industry, impacting the health of workers in the long term. Our study can ease further research and popularize the use of wearable systems for ergonomics analysis allowing these workplace prevention systems to reach different industrial environments., Ministerio de Ciencia e Innovación (PID2021- 124515OA-I00), Junta de Castilla y León-Consejería de Empleo e Industria (INVESTUN/21/VA/0003), 061/196941

Smartwatches, fitness trackers, and the broader range of wearable technologies have experienced widespread adoption, resulting in a robust market characterized by an excessive quantity of diverse products. This growing collection of options may nevertheless, perplex athletes endeavoring to select the most suitable choice within this field of interest. The study aims to simplify the complex landscape of wearable technologies, focusing on smartwatches, fitness trackers, and their embedded sensors. Additionally, the study validates a few solutions that could offer an effective and cost-efficient solution for measuring cadence in athletes. The study further analyzes and categorizes widely-used commercial sports wearables, offering a matching selection of the most relevant devices for measuring key sports performance metrics. The final results presents a comparative analysis of the most accessible wearables on the market, with the aim of conducting a gap analysis that contrasts the key performance indicators (KPIs) relevant to runners with the capabilities of research-grade tools. This comparison assesses how effectively, accurately, and affordably current market products and sensors can measure athletes’ required KPIs., Smartklockor, träningsspårare och det bredare utbudet av bärbara teknologier har fått en bred användning, vilket resulterat i en robust marknad som kännetecknas av en alltför stor mängd olika produkter. Denna växande samling av alternativ kan ändå förvirra idrottare som strävar efter att välja det mest lämpliga valet inom detta intresseområde. Studien syftar till att förenkla det komplexa landskapet av bärbara teknologier, med fokus på smarta klockor, träningsspårare och deras inbyggda sensorer. Dessutom validerar studien några lösningar som kan erbjuda en effektiv och kostnadseffektiv lösning för att mäta kadens hos idrottare. Studien analyserar och kategoriserar vidare allmänt använda kommersiella sportkläder, och erbjuder ett matchande urval av de mest relevanta enheterna för att mäta viktiga sportprestandamått. De slutliga resultaten presenterar en jämförande analys av de mest tillgängliga bärbara produkterna på marknaden, med syftet att genomföra en gapanalys som kontrasterar nyckelprestandaindikatorerna (KPI:er) som är relevanta för löpare med kapaciteten hos forskningsverktyg. Den här jämförelsen bedömer hur effektivt, exakt och prisvärt aktuella marknadsprodukter och sensorer kan mäta idrottares nödvändiga nyckeltal.

The vertical jump test evaluates leg strength and explosiveness and has many applications, mainly in sports. The gold standard technologies for assessing jump test performance are force plates and optoelectronic motion capture systems. Their drawback is that they are expensive and non-portable. Previous research has found potential in inertial measurement units (IMU) as an affordable and portable alternative. However, applying this technology often involves manual data processing, which can be difficult without professional oversight. There is also a scarcity of studies regarding the optimal positioning of the IMU on the body to maximize the accuracy of measurements. This study therefore aimed to develop and validate a real-time, automated algorithm for vertical jump tests using IMU technology and investigating optimal sensor placement. The algorithm estimates and gives feedback on jump height, net impulse, and peak values of force, power, and velocity. For validation, 8 participants performed jump tests on force plates while wearing inertial sensors placed on the chest, hip, ankle, and heel. The algorithm identified jumps in real time with perfect or near-perfect accuracy across all sensor positions. The hip position provided the least systematic errors in its feedback values. However, the chest position exhibited the largest correlation to the gold standard force plate measures. Values measured from the chest also showed the highest within-session reliability, meaning that they were consistent across jumps performed by the same participant. The study shows potential for real-time usage of IMUs during jump tests when sensors are placed on the chest and correction equations are applied to deal with systematic bias., Vertikala hopptest mäter benstyrka och explosivitet och har många användningsområden, framförallt inom idrott. För att utvärdera ett hopptest används oftast kraftplattor och optoelektroniska motion capture system. Nackdelen med dessa är att de är dyra och icke-portabla. Forskning har visat potential för inertial measurement units (IMU) som ett prisvärt och portabelt alternativ. Denna metod kräver dock ofta manuell bearbetning av data, vilket kan vara svårt utan professionell översyn. Dessutom finns det få studier om var på kroppen en IMU bör placeras för bästa mätnoggrannhet. Denna studie syftade därför till att utveckla och validera en IMU-baserad automatiserad algoritm för vertikala hopptest, samt undersöka vilken den optimala sensorplaceringen är. Den utvecklade algoritmen beräknar och ger återkoppling på hopphöjd, impuls och toppvärden för kraft, effekt och hastighet. För att validera metoden genomfördes hopptest på kraftplattor med 8 deltagare som samtidigt bar IMU-sensorer på bröstet, höften, vristen och hälen. Algoritmen kunde identifiera hopp med perfekt eller nästintill perfekt precision från alla sensorpositioner. Höftpositionen gav minst systematiska fel i återkopplingsvärdena. Mätvärden från bröstpositionen uppvisade dock den starkaste korrelationen med kraftplattornas mätningar. Bröstpositionen hade också högst reliabilitet, vilket innebär att hopp genomförda av samma deltagare gav konsekventa värden. Studien visar potential för realtidsanvändning av IMU under hopptest, om sensorer placeras på bröstet och korrigeringsekvationer tillämpas för att hantera systematiska fel.

Introduktion: Ryggproblematik inom längdskidåkning har blivit ett omdiskuterat ämne inom idrottssektorn, detta i samband med att deltekniken stakning tagit allt större plats inom den klassiska skidåkningen. En regel om maximal stavlängd på 83% av åkarens kroppslängd har införts, samtidigt som studier visar att en längre stav än 83% visat fördelar för prestation. Förutom de prestationsrelaterade effekterna har forskning visat på att längre stavar ger mindre ledvinklar. En ökad grad flexion av ländryggen antas öka risken för att drabbas av diskbråck. Om stavlängden har betydelse för denna skulle optimering av stavlängd kunna bidra till att förebygga skador. Syfte: Syftet med denna experimentella pilotstudie var att jämföra rörelseomfång i ländryggen vid stakning med stavar i olika längder, samt hur stavlängder påverkar ledvinklar i knä, fot, höft, bäcken och thorakalrygg. Metod: En försöksperson rekryterades. Mätningen utfördes i en snöhall på uppmätt sträcka om 50m, vid stakning med stavlängderna 83%, 86% och 89% av kroppslängden. För mätning av rörelseomfång användes ett IMU-system. Insamlad data extraherades via Noraxon MR3 och bearbetades i Matlab för att få fram medelvärden av ledvinklar. Resultat: Lumbalflexionen var som störst vid 89% stavlängd (41,5°) och lägst vid 86% (36,3°). Minst ledvinklar i fot, knä, höft och bäcken uppmättes samtliga vid stavlängd 89%, där även thorakalflexionen var som minst. Konklusion: Resultaten indikerar att stavlängder inom 83-86% av kroppslängden minskar rörelseomfånget och flexionsgraden av ländryggen vid stakning. Ytterligare forskning behövs för att bekräfta sambandet mellan ländryggsflexion och stavlängd och om en optimal stavlängd skulle kunna verka skadeförebyggande.

The literature has yielded promising data over the past decade regarding the use of inertial sensors for the analysis of occupational ergonomics. However, despite their significant advantages (e.g., portability, lightness, low cost, etc.), their widespread implementation in the actual workplace has not yet been realized, possibly due to their discomfort or potential alteration of the worker’s behaviour. This systematic review has two main objectives: (i) to synthesize and evaluate studies that have employed inertial sensors in ergonomic analysis based on the RULA method; and (ii) to propose an evaluation system for the transparency of this technology to the user as a potential factor that could influence the behaviour and/or movements of the worker. A search was conducted on the Web of Science and Scopus databases. The studies were summarized and categorized based on the type of industry, objective, type and number of sensors used, body parts analysed, combination (or not) with other technologies, real or controlled environment, and transparency. A total of 17 studies were included in this review. The Xsens MVN system was the most widely used in this review, and the majority of studies were classified with a moderate level of transparency. It is noteworthy, however, that there is a limited and worrisome number of studies conducted in uncontrolled real environments.

The interplay of biomechanical and neuromuscular aspects aids in the conversion of power production in running performance, and when combined with metabolic factors, these aspects may lead to improved running economy (RE). Therefore, the purpose of this study is to determine the association between the Stryd Power Meter foot pod metrics with RE and performance. Fifteen high-caliber male athletes completed two treadmill running sessions. First, RE was determined at 10 and 12 km/h. Second, two all-out efforts of nine and three minute duration were completed. At 10 km/h, the simple and stepwise multiple linear regression analysis revealed that form power (FP) was the best RE predictor (adjusted R2 = 0.464; p = 0.005) followed by vertical oscillation (VO) (R2 = 0.424; p = 0.005) and ground contact time (GCT) (R2 = 0.363; p = 0.010). At 12 km/h, such regressions confirmed that GCT was the best RE indicator (adjusted R2 = 0.222; p = 0.043) followed by FP (adjusted R2 = 0.213; p = 0.047). During the nine and three minute all-out effort sessions, GCT was the best performance predictor (R2 = 0.491; p = 0.002 and R2 = 0.380; p = 0.014, respectively). The biomechanical factors of GCT and FP are good indicators of RE and running performance.

Highlighting the crucial role of monitoring and quantifying lumbopelvic rhythm for spinal curvature, the Ergotex IMU, a portable, lightweight, cost-effective, and energy-efficient technology, has been specifically designed for the pelvic and lumbar area. This study investigates the concurrent validity of the Ergotex device in measuring sagittal pelvic tilt angle. We utilized an observational, repeated measures design with healthy adult males (mean age: 39.3 ± 7.6 y, body mass: 82.2 ± 13.0 kg, body height: 179 ± 8 cm), comparing Ergotex with a 3D optical tracking system. Participants performed pelvic tilt movements in anterior, neutral, and posterior conditions. Statistical analysis included paired samples t-tests, Bland–Altman plots, and regression analysis. The findings show minimal systematic error (0.08° overall) and high agreement between the Ergotex and optical tracking, with most data points falling within limits of agreement of Bland–Altman plots (around ±2°). Significant differences were observed only in the anterior condition (0.35°, p < 0.05), with trivial effect sizes (ES = 0.08), indicating that these differences may not be clinically meaningful. The high Pearson’s correlation coefficients across conditions underscore a robust linear relationship between devices (r > 0.9 for all conditions). Regression analysis showed a standard error of estimate (SEE) of 1.1° with small effect (standardized SEE < 0.26 for all conditions), meaning that the expected average deviation from the true value is around 1°. These findings validate the Ergotex as an effective, portable, and cost-efficient tool for assessing sagittal pelvic tilt, with practical implications in clinical and sports settings where traditional methods might be impractical or costly.

Introduktion: Ryggproblematik inom längdskidåkning har blivit ett omdiskuterat ämne inom idrottssektorn, detta i samband med att deltekniken stakning tagit allt större plats inom den klassiska skidåkningen. En regel om maximal stavlängd på 83% av åkarens kroppslängd har införts, samtidigt som studier visar att en längre stav än 83% visat fördelar för prestation. Förutom de prestationsrelaterade effekterna har forskning visat på att längre stavar ger mindre ledvinklar. En ökad grad flexion av ländryggen antas öka risken för att drabbas av diskbråck. Om stavlängden har betydelse för denna skulle optimering av stavlängd kunna bidra till att förebygga skador. Syfte: Syftet med denna experimentella pilotstudie var att jämföra rörelseomfång i ländryggen vid stakning med stavar i olika längder, samt hur stavlängder påverkar ledvinklar i knä, fot, höft, bäcken och thorakalrygg. Metod: En försöksperson rekryterades. Mätningen utfördes i en snöhall på uppmätt sträcka om 50m, vid stakning med stavlängderna 83%, 86% och 89% av kroppslängden. För mätning av rörelseomfång användes ett IMU-system. Insamlad data extraherades via Noraxon MR3 och bearbetades i Matlab för att få fram medelvärden av ledvinklar. Resultat: Lumbalflexionen var som störst vid 89% stavlängd (41,5°) och lägst vid 86% (36,3°). Minst ledvinklar i fot, knä, höft och bäcken uppmättes samtliga vid stavlängd 89%, där även thorakalflexionen var som minst. Konklusion: Resultaten indikerar att stavlängder inom 83-86% av kroppslängden minskar rörelseomfånget och flexionsgraden av ländryggen vid stakning. Ytterligare forskning behövs för att bekräfta sambandet mellan ländryggsflexion och stavlängd och om en optimal stavlängd skulle kunna verka skadeförebyggande.

Inertial measurement units (IMUs) are key components of various applications including navigation, robotics, aerospace, and automotive systems. IMU sensor characteristics have a significant impact on the accuracy and reliability of these applications. In particular, noise characteristics and bias stability are critical for proper filter settings to perform a combined GNSS/IMU solution. This paper presents an analysis based on the Allan deviation of different IMU sensors that correspond to different grades of micro-electromechanical systems (MEMS)-type IMUs in order to evaluate their accuracy and stability over time. The study covers three IMU sensors of different grades (ascending order): Rokubun Argonaut navigator sensor (InvenSense TDK MPU9250), Samsung Galaxy Note10 phone sensor (STMicroelectronics LSM6DSR), and NovAtel PwrPak7 sensor (Epson EG320N). The noise components of the sensors are computed using overlapped Allan deviation analysis on data collected over the course of a week in a static position. The focus of the analysis is to characterize the random walk noise and bias stability, which are the most critical for combined GNSS/IMU navigation and may differ or may not be listed in manufacturers’ specifications. Noise characteristics are calculated for the studied sensors and examples of their use in loosely coupled GNSS/IMU processing are assessed. This work proposes a structured and reproducible approach for working with sensors for their use in navigation tasks in combination with GNSS, and can be used for sensors of different levels to supplement missing or incorrect sensor manufacturers’ data., This research was funded by Rokubun S.L. and Universitat Politècnica de Catalunya with industrial PhD grant number 2020 DI 108 from the Generalitat de Catalunya; This research was partially funded by the Spanish Ministry of Science and Innovation and European Union FEDER through projects CNS2022-135383 and PID2022-138485OB-I00., Peer Reviewed, Postprint (published version)

A user's movement path can be precisely and concisely described as a concatenation of straight lines having the user's turns as their end points. Learning such a path description or representation from inertial measurement unit (IMU) sensors enables various mobile and IoT applications, as it allows efficient processing of the movement path data. It is, however, non-trivial to learn a succinct yet accurate path description from IMU sensor readings in the mobile device of a moving user on the fly due to the dynamically changing behaviors and the technical difficulty in detecting the user's turns. We propose PATHLIT, a novel online path description learning system based on IMU signals. PATHLIT learns position vectors of a user from IMU sensor readings by our custom-made self-attention network model. Once each position vector is learned, PATHLIT also decides whether or not to take it as a part of the resulting path description by our efficient online algorithm developed under the minimum description length principle, which essentially detects the user's turns along the path. We conduct extensive experiments on two large datasets. The experiment results show that PATHLIT achieves superior performance over state-of-the-art algorithms by up to 50% in absolute trajectory error using only 15% of trajectory data points. IEEE

L‘analyse de la qualité de la marche est un bon indicateur de la santé et de l’autonomie d’un individu et de sa capacité à effectuer ses tâches de la vie quotidienne. Les centrales inertielles émergent comme une alternative aux équipements de mesures traditionnels de la marche qui sont peu utilisés en clinique en raison de leur coût d’acquisition élevé, du temps d’installation requis et des limites de l’environnement de capture. Cependant, les méthodes d’analyse actuellement utilisées ne sont pas optimales pour ces capteurs puisqu’elles reposent sur des mesures de positions pour déterminer les paramètres spatio-temporels de la marche.Le modèle sigma-lognormal est basé sur la théorie cinématique des mouvements rapides, qui utilise des paramètres analogues au système neuromusculaire pour décrire la vitesse durant le mouvement sous forme de sommation d’impulsions. Bien que populaire pour décrire plusieurs mouvements, l’applicabilité du modèle sigma-lognormal à l’analyse de la marche n’a pas été démontré. Afin d’investiguer son potentiel, cette approche a été appliquée à des essais de marche sur tapis roulant à différentes vitesses, réalisés avec une population saine et atteinte d’arthrite psoriasique. Ces essais ont été capturés à l’aide d’un système optoélectronique, la référence dans le domaine, afin de valider le potentiel du modèle tout en limitant l’impact de la précision du système de capture de données.

Positional knowledge is important for any aerial vehicle. The information enables both detailed surveillance and exploration of the surrounding environment. In recent decades, the usage and development of unmanned aerial vehicles (UAVs) has drastically increased in both civilian and military applications. Making them act autonomously is also of great interest, which is a considerable research field of its own. Precise positioning is especially important in this scenario. Currently, global navigation satellite systems (GNSS), e.g. the American global positioning system (GPS), are mostly used to acquire the positional information of UAVs in outdoor environments. However, these satellite-based systems are sensitive to occlusions and jamming. Also, the systems are mostly operated by nations which could be a potential vulnerability. Therefore, backup navigation systems are of great importance now and certainly in the future. In this thesis, an outdoor UAV positioning system utilizing different sensors is proposed. More specifically, we employ radio beacons to measure angle of arrival (AOA) and received signal strength (RSS) from a transmitting UAV. Also, the onboard inertial measurement unit (IMU) and altimeter are used to provide further information of the dynamic behavior for robust estimation. First, a system design for tracking a UAV is proposed utilizing Kalman filters (KFs), where a decoupled tracker separating the orientation, using the IMU only, and position estimation is incorporated. Further, static positioning using nonlinear and linearized estimators are introduced for proper tracking filter initialization. Moreover, we develop a simultaneous localization and mapping (SLAM) framework to handle uncertainties in radio beacon position and orientation. To evaluate the proposed positioning framework, extensive real-world experiments are conducted to analyze maximum measurement range and noise level of measurements of the radio beacons. Further, the system performa, Opponent: William Nordström

The study of human locomotion, known as gait analysis, has for a long time been performed withexpensive equipment in laboratory settings. However, the emergence of machine learning sparkedinterest in integrating this technology in gait analysis, thus simplifying the process. This study’saim is to substitute the pressure insoles used during gait cycle analysis of a walking subject, with amachine learning model.To achieve this, a model based on Long-Short Term Memory networks that predicts vertical groundreaction force based on data from inertial measurement unit sensors was used. This serves as asubstitution for pressure insoles or pressure plates. The model was trained with time series datasetscontaining inertial measurement unit data and corresponding pressure insole data. Subsequently, itwas tested for intersubjective, out-of-sample data.The model was able to capture the periodicity of the gait cycle as well as predict the general shapeof the vertical ground reaction force curves, where the accuracy was quantified using normalisedroot mean squared error. The error was in a range between 17.8% and 13.4% and had an average of15.2%, when tested intersubjectively and out-of-sample. The most significant factor contributing tothe error was the model’s amplitude inaccuracies which was, most likely, due to information beinglost during the processing of the data, as well as simply having an insufficient amount of data.

This thesis aims to enhance analysis of Inertial Measurement Unit (IMU) data in context of professional forestry operations. The experiment involved mounting an IMU on a passive exoskeleton, capturing real-time movement data during forestry activity, and then the data was analysed both with and without machine learning. As a result, different back angle movement patterns has been identified using graphs on Euler angles which came from IMU sensor. And also AI model had over 60% of accuracy for all movements except for one, indicating a need for refinement, through more participants, better sensor attachment and combining other sensors. The findings from this thesis will guide future research, particularly for ergonomic specialist students and also mechanical students to develop an active exoskeleton. In conclusion, using IMU data with exoskeletons can improve worker safety and reduce ergonomic risks in forestry by informing the user of their posture.

Road roughness is the primary source of vehicle vibrations. This thesis investigates model-based methods for estimating road roughness in terms of the International Roughness Index (IRI) by measuring the chassis vibrations of the vehicle. This can provide NIRA Dynamics AB with a cost-effective pavement monitoring solution. Initially, system identification is performed on a physical car to estimate model parameters that reflect reality. Subsequently, two model-based IRI estimation methods are developed. One method relies on a transfer function between vertical chassis vibrations and the IRI according to a quarter-car model. The second method aims first to estimate the longitudinal road profile using a Kalman filter, and then calculate the IRI values from the estimated profile. This method can be implemented computationally efficiently and also offers the possibility of estimating the IRI using lateral vibrations. Both methods are validated using real-world data, and their performance is similar when using vertical vibrations, with the IRI estimation error’s standard deviation being roughly 10% to 20% of the reference value. However, the results are considerably worse when the estimation is purely based on lateral vibrations, indicating that lateral vibrations are not feasible for model-based IRI estimation, and the reasons for this are discussed.

Modern indoor positioning systems encounter challenges inherent to indoor environments. Signal changes can stem from various factors like object movement, signal propagation, or obstructed line of sight. This thesis explores a supervised machine learning approach that integrates Bluetooth Low Energy (BLE) and inertial sensor data to achieve consistent angle and distance estimations. The method relies on BLE angle estimations and signal strength alongside additional sensor data from an Inertial Measurement Unit (IMU). Relevant features are extracted and a supervised learning model is trained and then validated on familiar environment tests. The model is then gradually introduced to more unfamiliar test environments, and its performance is evaluated and compared accordingly. This thesis project was conducted at the u-blox office and presents a comprehensive methodology utilizing their existing hardware. Several extensive experiments were conducted, refining both data collection procedures and experimental setups. This iterative approach facilitated the improvement of the supervised learning model, resulting in a proposed model architecture based on transformers and convolutional layers. The provided methodology encompasses the entire process, from data collection to the evaluation of the proposed supervised learning model, enabling direct comparisons with existing angle estimation solutions employed at u-blox. The results of these comparisons demonstrate more accurate outcomes compared to existing solutions when validated in familiar environments. However, performance gradually declines when introduced to a new environment, encountering a wider range of signal conditions than the supervised model had trained on. Distance estimations are then compared with the path loss propagation equation, showing an overall improvement., Moderna inomhuspositioneringssystem möter utmaningar som förekommer i inomhusmiljöer. Signalförändringar kan bero på olika faktorer som objektets rörelse, signalutbredning eller blockerad siktlinje. Denna kandidat avhandling undersöker ett övervakat maskininlärningssätt som integrerar Bluetooth Low Energy (BLE) och tröghetssensorer för att uppnå konsekventa vinkel- och avståndsberäkningar. Metoden bygger på BLE-vinkelberäkningar och signalstyrka tillsammans med ytterligare sensordata från en Inertial Measurment Unit (IMU). Relevanta funktioner extraheras och en övervakad inlärningsmodell tränas och valideras sedan på tester i bekanta miljöer. Modellen introduceras sedan gradvis till mer obekanta testmiljöer, och dess prestanda utvärderas och jämförs därefter. Detta examensarbete genomfördes på u-blox kontor och presenterar en omfattande metodik som utnyttjar deras befintliga hårdvara. Flera omfattande experiment genomfördes, vilket förfinade både datainsamlingsprocedurer och experimentuppsättningar. Detta iterativa tillvägagångssätt underlättade förbättringen av den övervakade inlärningsmodellen, vilket resulterade i en föreslagen modellarkitektur baserad på transformatorer och konvolutionella lager. Den tillhandahållna metodiken omfattar hela processen, från datainsamling till utvärdering av den föreslagna övervakade inlärningsmodellen, vilket möjliggör direkta jämförelser med befintliga vinkelberäkningslösningar som används på u-blox. Resultaten av dessa jämförelser visar mer exakta resultat jämfört med befintliga lösningar när de valideras i bekanta miljöer. Dock minskar prestandan gradvis när den introduceras till en ny miljö, där den möter ett bredare spektrum av signalförhållanden än vad inlärningsmodellen har tränats på. Avståndsberäkningar jämförs sedan med en matematisk formel, kallat path loss propagation ekvationen, som ger distans som en funktion av uppmätt signalstyrka.

Detta examensarbete har genomförts med syftet att utveckla Inno-x företagets system, som är avsett för vardagsidrottare för att mäta neuromuskulära aktiviteter i underkroppen med hjälp av modern teknologi. Systemet omfattar en tröghetsmätningsenhet (IMU) med accelerometer, gyroskop och en EMG-sensor (elektromyografi). Denna konfiguration möjliggör noggrann övervakning av neuromuskulära aktiviteter genom analys av svar på träning. Studiens mål var att identifiera en effektiv sensor för mätning av horisontella hoppavstånd och att utveckla en algoritm som sedan ska integreras i företagets produkt. Produkten kommer att använda magnetometer och IMU för att tolka mänskliga rörelser och för att förbättra noggrannheten i företagets mätningssystem. Processen inkluderar förbättring av mätningarnas noggrannhet, integration av teknik med biomekaniska principer, utvärdering av kalibreringstekniker för magnetometeravläsningar, kombination av sensorer för rörelseanalys och genomförande av utvärdering med olika åldersgrupper som består av 10 deltagare för att bedöma systemets effektivitet. Även om ingen av metoderna helt uppnådde den önskade noggrannheten inom ±5 cm, visade alla metoder god prestanda för olika tillämpningar. Detta antyder att implementeringen av en kalibrerad magnetometer potentiellt kan förbättra systemets noggrannhet vid bestämning av horisontella hoppavstånd, dock endast med en liten marginal, eftersom studien visade att med kalibrerade magnetometer RMSE (Root Mean Square Error) ökat med 0.99 cm. Ytterligare forskning rekommenderas för att undersöka nya sätt att kalibrera sensorer och integrera dem för mer precisa avläsningar. Dock bör det beaktas att magnetometeravläsningar påverkas av miljöfaktorer. Dessutom är det viktigt att skapa ett användarvänligt gränssnitt som gör det möjligt för idrottare att enkelt spåra och analysera sina prestandadata., This thesis has been conducted with the objective of developing the Inno-X company's system, which is intended for everyday athletes to measure neuromuscular activities in the lower body using modern technology. The system includes an Inertial Measurement Unit (IMU) with an accelerometer, gyroscope, and an Electromyography (EMG) sensor. This configuration enables accurate monitoring of neuromuscular activities through the analysis of responses to training. The study's goal was to identify an effective sensor for measuring horizontal jump distances and to develop an algorithm that would then be integrated into the company's product. The product will use a magnetometer and IMU to interpret human movements and to improve the accuracy of the company's measurement system. The process includes improving the accuracy of measurements, integrating technology with biomechanical principles, evaluating calibration techniques for magnetometer readings, combining sensors for motion analysis, and conducting evaluations with different age groups consisting of 10 participants to assess the system's effectiveness. Although none of the methods fully achieved the desired accuracy within ±5 cm, all methods showed good performance for various applications. This suggests that the implementation of a calibrated magnetometer could potentially improve the system's accuracy in determining horizontal jump distances, albeit only by a small margin, as the study showed that with calibrated magnetometers, the Root Mean Square Error (RMSE) increased by 0.99 cm. Further research is recommended to explore new ways to calibrate sensors and integrate them for more precise readings. However, it should be considered that magnetometer readings are affected by environmental factors. Additionally, it is important to create a user-friendly interface that enables athletes to easily track and analyze their performance data.

Speed skating technique and movement has been previously analysed, however, there is a notable absence of a real-time feedback system that provides athletes with quantitative insights into their performance. Therefore, this thesis aims to identify and measure the Key Performance Parameters (KPPs) and create a framework of a feedback system that allows coaches and athletes to receive real-time feedback during training sessions. To achieve it, IMU data of skaters were recorded, and algorithms were developed to quantify KPPs. Furthermore, an interview was conducted with a speed skater to determine the optimal presentation format for feedback. To validate the accuracy of the KPP detection algorithms, video data capturing skating bouts were used as a reference. The algorithm was able to detect the contact and air phases with a mean difference of .002 seconds, while paired samples t-test showed no significant differences in contact (p=.842) and air (p=.815) phases. Although the estimation of skate lean angle presented challenges, promising indications for its potential were observed. Through detailed investigation, the optimal mode, content, timing, and frequency of feedback delivery was identified. Consequently, this thesis has laid the foundation for an evolutionary tool for speed skating training and performance assessment. The combination of IMU technology with advanced algorithms and feedback mechanisms has the potential to revolutionise how athletes approach their training., Teknik och rörelse inom hastighetsåkning har tidigare analyserats, men det finns en märkbar frånvaro av ett realtidsåterkopplingssystem som ger idrottare kvantitativa insikter i deras prestation. Därför syftar denna avhandling till att identifiera och mäta nyckelparametrar för prestanda (KPPs) och skapa ett ramverk för ett återkopplingssystem som tillåter tränare och idrottare att få realtidsåterkoppling under träningspass. För att uppnå detta registrerades IMU data från åkare och algoritmer utvecklades för att kvantifiera KPPs. Dessutom genomfördes en intervju med en hastighetsåkare för att fastställa det optimala presentationsformatet för återkoppling. För att validera noggrannheten hos algoritmerna för KPP-detektion användes videodata som fångade åksessioner som referens. Algoritmen kunde upptäcka kontakt- och luftfaserna med en medeldifferens på .002 sekunder, medan ett parat t-test visade inga signifikanta skillnader i kontakt (p=.842) och luft (p=.815) faser. Även om uppskattningen av skatens lutningsvinkel presenterade utmaningar observerades lovande indikationer för dess potential. Genom detaljerad undersökning identifierades det optimala läget, innehållet, tidpunkten och frekvensen för återkopplingsleverans. Följaktligen har denna avhandling lagt grunden för ett evolutionärt verktyg för hastighetsåkningsträning och prestationsbedömning. Kombinationen av IMU-teknologi med avancerade algoritmer och återkopplingsmekanismer har potential att revolutionera hur idrottare närmar sig sin träning.

Self-motion involves the perception and awareness of one's own movement through three-dimensional space and is important for maintaining balance and spatial orientation. Self-motion sensations can be triggered using Galvanic Vestibular Stimulation (GVS), which stimulates the vestibular system, resulting in sensations of motion. Current research shows limitations in assessing perceived self-motion induced by GVS, relying on complex setups that are hard to replicate. This project develops a methodology to quantify perceived self-motion and provides a replicable setup for future research. The primary aim of this project was to develop and validate a methodology which allows us to objectively quantify perceived self-motion induced by GVS. The secondary goal was to investigate any correlation between GVS intensity and the velocity of perceived self-motion. To achieve these aims, the first step included translating the perceived self-motion into a voluntary hand movement, reflecting the perceived self-motion. The second step consisted of measuring the velocity of the hand movement, representing the velocity of perceived self-motion, by using two systems: an Inertial Measurement Unit (IMU) and a Motion Tracking Camera System (MTCS). These two systems were then used to compare and validate each other’s results. The study applied direct current (DC) intensities ranging from two to six mA with electrodes placed on the mastoid bones in a bipolar bilateral configuration to assess perceived self-motion. The developed methodology was used in a pilot trial to investigate any correlation between GVS current intensity and perceived self-motion velocity. Six healthy participants joined the trial, one of whom withdrew due to discomfort caused by GVS. When the velocity data was averaged across all subjects, the data suggested an underlying trend between GVS intensity and hand velocity. While there was considerable individual variability in hand velocity, a trend towards a moderate corr, Uppfattning om självrörelse innefattar medvetenheten om ens egna rörelser i det tredimensionella rummet och är viktig för att upprätthålla balans och rumslig orientering. Galvanisk vestibulär stimulering (GVS) kan framkalla en illusion av självrörelse genom att stimulera det vestibulära systemet. Nuvarande forskning visar begränsningar i att undersöka självrörelseuppfattning framkallad av GVS och förlitar sig på komplexa experimentalla uppställningar som är svåra att replikera. Detta projekt utvecklar en metod för att kvantifiera självrörelseuppfattning och tillhandahålla en replikerbar uppställning för framtida forskning. Det primära syftet med detta projekt var att utveckla och validera en metod som möjliggör objektiv mätning och kvantifiering av självrörelseuppfattning framkallad av GVS. Det sekundära målet var att undersöka eventuella korrelationer mellan GVS-intensitet och uppfattning av självrörelsehastighet. För att uppnå dessa mål var metodens första steg att omvandla självrörelseuppfattningen till en handrörelse, som avspeglar rörelseuppfattningen. Metodens andra steg bestod av att mäta handrörelsehastigheten, som representerar den upplevda självrörelsehastigheten, genom att använda två system: en IMU (Inertial Measurement Unit) och ett kamerasystem för rörelseregistrering (MTCS). Dessa två system användes sedan för att jämföra och validera varandras resultat. Studien tillämpade likströmsintensiteter från två till sex mA med elektroder placerade på mastoidutskotten i en bipolär bilateral konfiguration för att bedöma upplevd självrörelse. Den utvecklade metoden användes i en pilotstudie för att undersöka eventuella korrelationer mellan GVS-intensitet och upplevd självrörelsehastighet. Sex friska försökspersoner deltog i studien, varav en drog sig ur på grund av obehag orsakad av GVS. När hastighetsdatan från alla försökspersoner sammanställdes till ett genomsnittligt medelvärde, antydde datan en underliggande trend mellan GVS-intensitet och handrörelsehastig

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75–0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements., QC 20240725

This dataset contains samples coming from a hydraulic block from a biomedical equipment. The block mounts 3 Thomas SR10/30 DC standard perisltaltic pumps, which were filled with distilled water. Only one pump was running at the same time during these recordings, always at maximum constant speed. The cassettes of the pumps were changed before each recording. We used cassettes with 2 different levels of degradation: NEW (unused) and OLD (lifetime already expired). We defined 3 different classes: Class 1 is STOP (no pump running), class 2 is NEW (one pump running with a new cassette), and class 3 is OLD (one pump running with an old cassette). The classified samples were recorded using several sensors: 3 accelerometers, 1 gyroscope, 1 magnetometer and 1 microphone. All data were recorded at the same time at the maximum available frequency using the device "ST SensorTile.box". The raw data has already been processed into sepparate different .csv files (.wav files for audio) using python code.

Self-motion involves the perception and awareness of one's own movement through three-dimensional space and is important for maintaining balance and spatial orientation. Self-motion sensations can be triggered using Galvanic Vestibular Stimulation (GVS), which stimulates the vestibular system, resulting in sensations of motion. Current research shows limitations in assessing perceived self-motion induced by GVS, relying on complex setups that are hard to replicate. This project develops a methodology to quantify perceived self-motion and provides a replicable setup for future research. The primary aim of this project was to develop and validate a methodology which allows us to objectively quantify perceived self-motion induced by GVS. The secondary goal was to investigate any correlation between GVS intensity and the velocity of perceived self-motion. To achieve these aims, the first step included translating the perceived self-motion into a voluntary hand movement, reflecting the perceived self-motion. The second step consisted of measuring the velocity of the hand movement, representing the velocity of perceived self-motion, by using two systems: an Inertial Measurement Unit (IMU) and a Motion Tracking Camera System (MTCS). These two systems were then used to compare and validate each other’s results. The study applied direct current (DC) intensities ranging from two to six mA with electrodes placed on the mastoid bones in a bipolar bilateral configuration to assess perceived self-motion. The developed methodology was used in a pilot trial to investigate any correlation between GVS current intensity and perceived self-motion velocity. Six healthy participants joined the trial, one of whom withdrew due to discomfort caused by GVS. When the velocity data was averaged across all subjects, the data suggested an underlying trend between GVS intensity and hand velocity. While there was considerable individual variability in hand velocity, a trend towards a moderate corr, Uppfattning om självrörelse innefattar medvetenheten om ens egna rörelser i det tredimensionella rummet och är viktig för att upprätthålla balans och rumslig orientering. Galvanisk vestibulär stimulering (GVS) kan framkalla en illusion av självrörelse genom att stimulera det vestibulära systemet. Nuvarande forskning visar begränsningar i att undersöka självrörelseuppfattning framkallad av GVS och förlitar sig på komplexa experimentalla uppställningar som är svåra att replikera. Detta projekt utvecklar en metod för att kvantifiera självrörelseuppfattning och tillhandahålla en replikerbar uppställning för framtida forskning. Det primära syftet med detta projekt var att utveckla och validera en metod som möjliggör objektiv mätning och kvantifiering av självrörelseuppfattning framkallad av GVS. Det sekundära målet var att undersöka eventuella korrelationer mellan GVS-intensitet och uppfattning av självrörelsehastighet. För att uppnå dessa mål var metodens första steg att omvandla självrörelseuppfattningen till en handrörelse, som avspeglar rörelseuppfattningen. Metodens andra steg bestod av att mäta handrörelsehastigheten, som representerar den upplevda självrörelsehastigheten, genom att använda två system: en IMU (Inertial Measurement Unit) och ett kamerasystem för rörelseregistrering (MTCS). Dessa två system användes sedan för att jämföra och validera varandras resultat. Studien tillämpade likströmsintensiteter från två till sex mA med elektroder placerade på mastoidutskotten i en bipolär bilateral konfiguration för att bedöma upplevd självrörelse. Den utvecklade metoden användes i en pilotstudie för att undersöka eventuella korrelationer mellan GVS-intensitet och upplevd självrörelsehastighet. Sex friska försökspersoner deltog i studien, varav en drog sig ur på grund av obehag orsakad av GVS. När hastighetsdatan från alla försökspersoner sammanställdes till ett genomsnittligt medelvärde, antydde datan en underliggande trend mellan GVS-intensitet och handrörelsehastig

Modern indoor positioning systems encounter challenges inherent to indoor environments. Signal changes can stem from various factors like object movement, signal propagation, or obstructed line of sight. This thesis explores a supervised machine learning approach that integrates Bluetooth Low Energy (BLE) and inertial sensor data to achieve consistent angle and distance estimations. The method relies on BLE angle estimations and signal strength alongside additional sensor data from an Inertial Measurement Unit (IMU). Relevant features are extracted and a supervised learning model is trained and then validated on familiar environment tests. The model is then gradually introduced to more unfamiliar test environments, and its performance is evaluated and compared accordingly. This thesis project was conducted at the u-blox office and presents a comprehensive methodology utilizing their existing hardware. Several extensive experiments were conducted, refining both data collection procedures and experimental setups. This iterative approach facilitated the improvement of the supervised learning model, resulting in a proposed model architecture based on transformers and convolutional layers. The provided methodology encompasses the entire process, from data collection to the evaluation of the proposed supervised learning model, enabling direct comparisons with existing angle estimation solutions employed at u-blox. The results of these comparisons demonstrate more accurate outcomes compared to existing solutions when validated in familiar environments. However, performance gradually declines when introduced to a new environment, encountering a wider range of signal conditions than the supervised model had trained on. Distance estimations are then compared with the path loss propagation equation, showing an overall improvement., Moderna inomhuspositioneringssystem möter utmaningar som förekommer i inomhusmiljöer. Signalförändringar kan bero på olika faktorer som objektets rörelse, signalutbredning eller blockerad siktlinje. Denna kandidat avhandling undersöker ett övervakat maskininlärningssätt som integrerar Bluetooth Low Energy (BLE) och tröghetssensorer för att uppnå konsekventa vinkel- och avståndsberäkningar. Metoden bygger på BLE-vinkelberäkningar och signalstyrka tillsammans med ytterligare sensordata från en Inertial Measurment Unit (IMU). Relevanta funktioner extraheras och en övervakad inlärningsmodell tränas och valideras sedan på tester i bekanta miljöer. Modellen introduceras sedan gradvis till mer obekanta testmiljöer, och dess prestanda utvärderas och jämförs därefter. Detta examensarbete genomfördes på u-blox kontor och presenterar en omfattande metodik som utnyttjar deras befintliga hårdvara. Flera omfattande experiment genomfördes, vilket förfinade både datainsamlingsprocedurer och experimentuppsättningar. Detta iterativa tillvägagångssätt underlättade förbättringen av den övervakade inlärningsmodellen, vilket resulterade i en föreslagen modellarkitektur baserad på transformatorer och konvolutionella lager. Den tillhandahållna metodiken omfattar hela processen, från datainsamling till utvärdering av den föreslagna övervakade inlärningsmodellen, vilket möjliggör direkta jämförelser med befintliga vinkelberäkningslösningar som används på u-blox. Resultaten av dessa jämförelser visar mer exakta resultat jämfört med befintliga lösningar när de valideras i bekanta miljöer. Dock minskar prestandan gradvis när den introduceras till en ny miljö, där den möter ett bredare spektrum av signalförhållanden än vad inlärningsmodellen har tränats på. Avståndsberäkningar jämförs sedan med en matematisk formel, kallat path loss propagation ekvationen, som ger distans som en funktion av uppmätt signalstyrka.

Road roughness is the primary source of vehicle vibrations. This thesis investigates model-based methods for estimating road roughness in terms of the International Roughness Index (IRI) by measuring the chassis vibrations of the vehicle. This can provide NIRA Dynamics AB with a cost-effective pavement monitoring solution. Initially, system identification is performed on a physical car to estimate model parameters that reflect reality. Subsequently, two model-based IRI estimation methods are developed. One method relies on a transfer function between vertical chassis vibrations and the IRI according to a quarter-car model. The second method aims first to estimate the longitudinal road profile using a Kalman filter, and then calculate the IRI values from the estimated profile. This method can be implemented computationally efficiently and also offers the possibility of estimating the IRI using lateral vibrations. Both methods are validated using real-world data, and their performance is similar when using vertical vibrations, with the IRI estimation error’s standard deviation being roughly 10% to 20% of the reference value. However, the results are considerably worse when the estimation is purely based on lateral vibrations, indicating that lateral vibrations are not feasible for model-based IRI estimation, and the reasons for this are discussed.

Positional knowledge is important for any aerial vehicle. The information enables both detailed surveillance and exploration of the surrounding environment. In recent decades, the usage and development of unmanned aerial vehicles (UAVs) has drastically increased in both civilian and military applications. Making them act autonomously is also of great interest, which is a considerable research field of its own. Precise positioning is especially important in this scenario. Currently, global navigation satellite systems (GNSS), e.g. the American global positioning system (GPS), are mostly used to acquire the positional information of UAVs in outdoor environments. However, these satellite-based systems are sensitive to occlusions and jamming. Also, the systems are mostly operated by nations which could be a potential vulnerability. Therefore, backup navigation systems are of great importance now and certainly in the future. In this thesis, an outdoor UAV positioning system utilizing different sensors is proposed. More specifically, we employ radio beacons to measure angle of arrival (AOA) and received signal strength (RSS) from a transmitting UAV. Also, the onboard inertial measurement unit (IMU) and altimeter are used to provide further information of the dynamic behavior for robust estimation. First, a system design for tracking a UAV is proposed utilizing Kalman filters (KFs), where a decoupled tracker separating the orientation, using the IMU only, and position estimation is incorporated. Further, static positioning using nonlinear and linearized estimators are introduced for proper tracking filter initialization. Moreover, we develop a simultaneous localization and mapping (SLAM) framework to handle uncertainties in radio beacon position and orientation. To evaluate the proposed positioning framework, extensive real-world experiments are conducted to analyze maximum measurement range and noise level of measurements of the radio beacons. Further, the system performa, Opponent: William Nordström

Speed skating technique and movement has been previously analysed, however, there is a notable absence of a real-time feedback system that provides athletes with quantitative insights into their performance. Therefore, this thesis aims to identify and measure the Key Performance Parameters (KPPs) and create a framework of a feedback system that allows coaches and athletes to receive real-time feedback during training sessions. To achieve it, IMU data of skaters were recorded, and algorithms were developed to quantify KPPs. Furthermore, an interview was conducted with a speed skater to determine the optimal presentation format for feedback. To validate the accuracy of the KPP detection algorithms, video data capturing skating bouts were used as a reference. The algorithm was able to detect the contact and air phases with a mean difference of .002 seconds, while paired samples t-test showed no significant differences in contact (p=.842) and air (p=.815) phases. Although the estimation of skate lean angle presented challenges, promising indications for its potential were observed. Through detailed investigation, the optimal mode, content, timing, and frequency of feedback delivery was identified. Consequently, this thesis has laid the foundation for an evolutionary tool for speed skating training and performance assessment. The combination of IMU technology with advanced algorithms and feedback mechanisms has the potential to revolutionise how athletes approach their training., Teknik och rörelse inom hastighetsåkning har tidigare analyserats, men det finns en märkbar frånvaro av ett realtidsåterkopplingssystem som ger idrottare kvantitativa insikter i deras prestation. Därför syftar denna avhandling till att identifiera och mäta nyckelparametrar för prestanda (KPPs) och skapa ett ramverk för ett återkopplingssystem som tillåter tränare och idrottare att få realtidsåterkoppling under träningspass. För att uppnå detta registrerades IMU data från åkare och algoritmer utvecklades för att kvantifiera KPPs. Dessutom genomfördes en intervju med en hastighetsåkare för att fastställa det optimala presentationsformatet för återkoppling. För att validera noggrannheten hos algoritmerna för KPP-detektion användes videodata som fångade åksessioner som referens. Algoritmen kunde upptäcka kontakt- och luftfaserna med en medeldifferens på .002 sekunder, medan ett parat t-test visade inga signifikanta skillnader i kontakt (p=.842) och luft (p=.815) faser. Även om uppskattningen av skatens lutningsvinkel presenterade utmaningar observerades lovande indikationer för dess potential. Genom detaljerad undersökning identifierades det optimala läget, innehållet, tidpunkten och frekvensen för återkopplingsleverans. Följaktligen har denna avhandling lagt grunden för ett evolutionärt verktyg för hastighetsåkningsträning och prestationsbedömning. Kombinationen av IMU-teknologi med avancerade algoritmer och återkopplingsmekanismer har potential att revolutionera hur idrottare närmar sig sin träning.

This thesis aims to enhance analysis of Inertial Measurement Unit (IMU) data in context of professional forestry operations. The experiment involved mounting an IMU on a passive exoskeleton, capturing real-time movement data during forestry activity, and then the data was analysed both with and without machine learning. As a result, different back angle movement patterns has been identified using graphs on Euler angles which came from IMU sensor. And also AI model had over 60% of accuracy for all movements except for one, indicating a need for refinement, through more participants, better sensor attachment and combining other sensors. The findings from this thesis will guide future research, particularly for ergonomic specialist students and also mechanical students to develop an active exoskeleton. In conclusion, using IMU data with exoskeletons can improve worker safety and reduce ergonomic risks in forestry by informing the user of their posture.

The study of human locomotion, known as gait analysis, has for a long time been performed withexpensive equipment in laboratory settings. However, the emergence of machine learning sparkedinterest in integrating this technology in gait analysis, thus simplifying the process. This study’saim is to substitute the pressure insoles used during gait cycle analysis of a walking subject, with amachine learning model.To achieve this, a model based on Long-Short Term Memory networks that predicts vertical groundreaction force based on data from inertial measurement unit sensors was used. This serves as asubstitution for pressure insoles or pressure plates. The model was trained with time series datasetscontaining inertial measurement unit data and corresponding pressure insole data. Subsequently, itwas tested for intersubjective, out-of-sample data.The model was able to capture the periodicity of the gait cycle as well as predict the general shapeof the vertical ground reaction force curves, where the accuracy was quantified using normalisedroot mean squared error. The error was in a range between 17.8% and 13.4% and had an average of15.2%, when tested intersubjectively and out-of-sample. The most significant factor contributing tothe error was the model’s amplitude inaccuracies which was, most likely, due to information beinglost during the processing of the data, as well as simply having an insufficient amount of data.

Combining inertial measurements with LiDAR measurements for odometry and mapping is ubiquitous, but low-cost inertial measurement units are often noisy sensors, and it is suspected that naive integration of the measurements will result in deteriorated performance. The project explores under which circumstances integration of inertial measurements is beneficial for the robustness and accuracy of LiDAR odometry and mapping. It is already known that inertial measurements have the potential to improve performance, but when and how are rarely the main topics of study in existing literature. This project analyzes one way inertial measurements are used to compensate for motion while registering points from a spinning LiDAR, and compares this to a similar method that does not use inertial measurements. It is found that integration of inertial measurements is beneficial in cases of fast rotations of the sensor and that in other cases, it does not make a significant difference. The results can be explained with existing theory, and hence provide confidence in the theory for predicting behavior in similar systems., Att kombinera tröghetsmätningar med LiDAR-mätningar för odometri och kartläggning är vanligt förekommande, men lågkostnadströghetsmätare är ofta brusiga sensorer och därmed misstänks att en naiv integrering av mätningarna skulle resultera i försämrad prestanda. Projektet utforskar i vilka fall det är gynnsamt att integrera tröghetsmätningar för LiDAR-odometri och kartläggning. Det är redan känt att tröghetsmätningar har potential att förbättra prestandan, men när och hur är sällan ett huvudområde i den befintliga litteraturen. Detta projekt undersöker ett sätt som tröghetsmätningar används på för att kompensera för rörelse i samband med registrering av punkter från en roterande LiDAR, och jämför detta med en liknande metod som inte använder tröghetsmätningar. Resultaten pekar på att integrering av tröghetsmätningar är gynnsamt i fall med hastiga rotationer av sensorn, i övriga fall observeras ingen avsevärd skillnad. Resultaten kan förklaras med befintlig teori, vilket styrker teorin och gör det möjligt att med högre tillit använda den för att förutse beteendet hos liknande system.

Detta examensarbete har genomförts med syftet att utveckla Inno-x företagets system, som är avsett för vardagsidrottare för att mäta neuromuskulära aktiviteter i underkroppen med hjälp av modern teknologi. Systemet omfattar en tröghetsmätningsenhet (IMU) med accelerometer, gyroskop och en EMG-sensor (elektromyografi). Denna konfiguration möjliggör noggrann övervakning av neuromuskulära aktiviteter genom analys av svar på träning. Studiens mål var att identifiera en effektiv sensor för mätning av horisontella hoppavstånd och att utveckla en algoritm som sedan ska integreras i företagets produkt. Produkten kommer att använda magnetometer och IMU för att tolka mänskliga rörelser och för att förbättra noggrannheten i företagets mätningssystem. Processen inkluderar förbättring av mätningarnas noggrannhet, integration av teknik med biomekaniska principer, utvärdering av kalibreringstekniker för magnetometeravläsningar, kombination av sensorer för rörelseanalys och genomförande av utvärdering med olika åldersgrupper som består av 10 deltagare för att bedöma systemets effektivitet. Även om ingen av metoderna helt uppnådde den önskade noggrannheten inom ±5 cm, visade alla metoder god prestanda för olika tillämpningar. Detta antyder att implementeringen av en kalibrerad magnetometer potentiellt kan förbättra systemets noggrannhet vid bestämning av horisontella hoppavstånd, dock endast med en liten marginal, eftersom studien visade att med kalibrerade magnetometer RMSE (Root Mean Square Error) ökat med 0.99 cm. Ytterligare forskning rekommenderas för att undersöka nya sätt att kalibrera sensorer och integrera dem för mer precisa avläsningar. Dock bör det beaktas att magnetometeravläsningar påverkas av miljöfaktorer. Dessutom är det viktigt att skapa ett användarvänligt gränssnitt som gör det möjligt för idrottare att enkelt spåra och analysera sina prestandadata., This thesis has been conducted with the objective of developing the Inno-X company's system, which is intended for everyday athletes to measure neuromuscular activities in the lower body using modern technology. The system includes an Inertial Measurement Unit (IMU) with an accelerometer, gyroscope, and an Electromyography (EMG) sensor. This configuration enables accurate monitoring of neuromuscular activities through the analysis of responses to training. The study's goal was to identify an effective sensor for measuring horizontal jump distances and to develop an algorithm that would then be integrated into the company's product. The product will use a magnetometer and IMU to interpret human movements and to improve the accuracy of the company's measurement system. The process includes improving the accuracy of measurements, integrating technology with biomechanical principles, evaluating calibration techniques for magnetometer readings, combining sensors for motion analysis, and conducting evaluations with different age groups consisting of 10 participants to assess the system's effectiveness. Although none of the methods fully achieved the desired accuracy within ±5 cm, all methods showed good performance for various applications. This suggests that the implementation of a calibrated magnetometer could potentially improve the system's accuracy in determining horizontal jump distances, albeit only by a small margin, as the study showed that with calibrated magnetometers, the Root Mean Square Error (RMSE) increased by 0.99 cm. Further research is recommended to explore new ways to calibrate sensors and integrate them for more precise readings. However, it should be considered that magnetometer readings are affected by environmental factors. Additionally, it is important to create a user-friendly interface that enables athletes to easily track and analyze their performance data.

In space exploration, CubeSats—small satellites of less than 10 centimeters—challenge conventional knowledge. Tightly packed into little more than the size of a shoebox, but with boundless potential nonetheless, these technical marvels are directing a revolution in the fight for space. Their revolutionary influence stems not only from their diminutive size but also from their extraordinary contribution to democratising utilisation of space, breaking through traditional barriers to understanding. The primary goal of this project is to improve a single aspect of a CubeSat prototype. Specifically, it is desired to push the boundaries of new methods in the field of Attitude Determination and Control Systems (ADCS). After a thorough evaluation of the prototype’s present state, special attention has been paid to resolving any identified performance flaws. The result of these efforts was the creation of new operating modes that are purposefully used to maximise the performance of the current control systems. Simultaneously, little problems like inflexible drive configurations, code optimisation, and the Inertial Measurement Unit’s (IMU) limited resolution were carefully fixed. These efforts produced groundbreaking outcomes that closed the door on enduring problems from earlier studies, like static errors in position control, and opened up new research directions. A number of significant findings support these improvements’ accuracy and efficacy. Moreover, the Simulink model that came before this project experienced a significant transformation. By achieving better block diagram arrangement and better parametrization, the model’s quality and readability have been greatly increased. Remarkably, the improved model showed almost indiscernible discrepancies between its output and actual experimental data, confirming the new model’s careful parameterization. This significant advancement highlights the project’s dedication to accuracy and creativity in the ever-evolving field of

The generation of a 3D map of an unseen environment, obtained through solving the SLAM problem, is a popular topic currently in the robotics domain. The Lunar Rover Mini (LRM) at the German Aerospace Center solves this problem using a RGB-D camera system, which is favourable in space applications due to its lightweight characteristics and energy-efficiency. Performing SLAM based on camera images is based on visual odometry: the science of estimating the rover’s trajectory trough a sequence of images. However, the dependency on a single sensor to perform mapping and navigation poses a threat to the reliability of the system. To increase the reliability and robustness of the SLAM algorithm, an inertial measurement unit (IMU) is incorporated in the robot hardware. This thesis describes the design for a visual-inertial SLAM algorithm that incorporates both visual and inertial measurements to solve the SLAM problem through performing tightly coupled sensor fusion, which estimates and corrects for IMU biases. The solution is based on a non-linear factor graph, which is a graphical model to represent the relationships between the rover’s measurements and the unknown variables which are optimised for. This is done using the open-source GTSAM framework. Using experimental data, the robustness of the novel visual-inertial SLAM algorithm is demonstrated by simulating specific sensor failures. Moreover, the novel algorithm shows its capability to incorporate a degree of certainty regarding specific areas of the generated map, closely resembling how a human being would generate a map of an unknown area. An additional use case for tightly coupled sensor fusion is the increased accuracy of the estimated trajectory. Assuming Gaussian noise models for both measurement models, averaging the two can yield a higher accuracy than either of the two sensors could have obtained by itself. This hypothesis was tested in another experiment. As the main mechanism behind bia, This thesis was conducted in close collaboration with the German Aerospace Center in Oberpfaffenhofen, Germany, Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

Combining inertial measurements with LiDAR measurements for odometry and mapping is ubiquitous, but low-cost inertial measurement units are often noisy sensors, and it is suspected that naive integration of the measurements will result in deteriorated performance. The project explores under which circumstances integration of inertial measurements is beneficial for the robustness and accuracy of LiDAR odometry and mapping. It is already known that inertial measurements have the potential to improve performance, but when and how are rarely the main topics of study in existing literature. This project analyzes one way inertial measurements are used to compensate for motion while registering points from a spinning LiDAR, and compares this to a similar method that does not use inertial measurements. It is found that integration of inertial measurements is beneficial in cases of fast rotations of the sensor and that in other cases, it does not make a significant difference. The results can be explained with existing theory, and hence provide confidence in the theory for predicting behavior in similar systems., Att kombinera tröghetsmätningar med LiDAR-mätningar för odometri och kartläggning är vanligt förekommande, men lågkostnadströghetsmätare är ofta brusiga sensorer och därmed misstänks att en naiv integrering av mätningarna skulle resultera i försämrad prestanda. Projektet utforskar i vilka fall det är gynnsamt att integrera tröghetsmätningar för LiDAR-odometri och kartläggning. Det är redan känt att tröghetsmätningar har potential att förbättra prestandan, men när och hur är sällan ett huvudområde i den befintliga litteraturen. Detta projekt undersöker ett sätt som tröghetsmätningar används på för att kompensera för rörelse i samband med registrering av punkter från en roterande LiDAR, och jämför detta med en liknande metod som inte använder tröghetsmätningar. Resultaten pekar på att integrering av tröghetsmätningar är gynnsamt i fall med hastiga rotationer av sensorn, i övriga fall observeras ingen avsevärd skillnad. Resultaten kan förklaras med befintlig teori, vilket styrker teorin och gör det möjligt att med högre tillit använda den för att förutse beteendet hos liknande system.

Autonomous navigation is a critical aspect of robotic systems, particularly in hostile and uncertain environments, and robot localization is central to navigation. Robot localization establishes its position within its surroundings. This thesis addresses the challenge of robot localization, focusing on a lunar-like environment with constraints such as limited computational resources and the use of non-visual based sensors. In this thesis, three sensors — wheel encoders (WE), Sun sensor (SS), and inertial measurement unit (IMU) — are employed for localization. Each sensor contributes distinct information regarding position and orientation. However, individual sensor measurements suffer from inherent inaccuracies and errors, especially the IMU’s reliance on integration over time, leading to significant drift. To mitigate these challenges, three fusion methods are explored: sensor selection based on predefined thresholds, Kalman filtering, and weighted fusion. Results indi- cate substantial improvements in localization accuracy compared to individual sensor measurements. The weighted fusion method, in particular, demonstrates superior per- formance by assigning appropriate importance, according to their accuracy, to each sensor’s information, resulting in significantly reduced positioning errors. The maxi- mum localization error using this method is 92m, which is smaller than reported in the literature. Further, the maximum localization percentage error over 65m is around 8%, which is comparable to the literature with visual sensors. The weighted fusion method introduces only a marginal increase in the computational complexity. Thus, this method stands out for its simplicity and delivers results superior to those documented in existing literature for non-visual sensors. Despite promising results, the research is met with certain hurdles, notably the avail- ability and consistency of datasets. The reliance on existing datasets, such as the Devon Island Rover dataset, hi, Electrical Engineering | Signals and Systems

Calibration of the inertial measuring unit (IMU) is an independent task and is the most important stage in preparing for operation both platform and strapdown inertial navigation systems (INS). The traditional calibration of IMU, containing at least three gyroscopes and three accelerometers, carried out in specialized testing laboratories, has been studied in sufficient detail. However, the widespread use of IMUs using MEMS sensors with characteristic instability of parameters leads to the need for additional calibration of IMUs IMUediately before INS operation in critical or field conditions. Under such conditions, firstly, there is no testing equipment necessary for the full implementation of traditional calibration, and secondly, the recalibration process itself is limited in time.In the article, studies were carried out on the possibility of using the method of successive rotations for the recalibration of IMU in the field. With this approach, in contrast to the test rotation method, there is no need to rotate the IMU by 180 degrees, but it is enough to rotate the IMU by small angles. Calculation ratios are obtained for determining the scale factors and zero signals of the accelerometer unit for two positions of the IMU. The experiment carried out confirmed the validity of the calculated ratios obtained. In addition, a recalibration method is described, which makes it possible to estimate, in addition to scale factors and zero signals of the accelerometer unit, also the tilt angles of the base on which the IMU is installed. To do this, you must have at least five different IMU positions.In addition, the calibration of the gyroscope unit when tactical and navigational accuracy class gyroscopes are used is considered. For gyroscopes of the tactical accuracy class and which are manufactured using MEMS technology, biases are determined in the critical conditions, which are not stable from start to start. Algorithms for determining biases and scale factors were

Home-based exercise is a popular physical activity of maintaining fitness, health andwellness in general. However, without proper supervision and basic knowledge of theexercises in the workout plan, there is an increased risk of injury. Considering that noteveryone is willing to attend crowded gyms or schedule professional personal trainingsessions, in this study, a novel feedback system is proposed, in the form of a mobileapplication. Accelerometer and gyroscope data were collected from 10 volunteersperforming 3 exercises, squats, lunges and bridges, with inertial sensors attachedto their back lumbar region, on both shanks and on both thighs. Each participantperformed 5 repetitions of the correct technique and 5 repetitions of 4 mistakes foreach exercise. The accuracies of 3 classifiers, a SVM, a RF and DT were comparedwith the SVM performing the best across all 3 exercises. The best location and numberof sensors was determined by examining the accuracy of a SVM model for 15 uniquemulti-sensor configurations. The best performing setup, being the configuration with 2sensors, one at the lumbar area and one at the shank, was used in exploring the efficacyof different data processing techniques. Time-domain statistical features, sensor angletimeseries and the filtered signal timeseries were evaluated as input to a NN. The timedomainfeatures performed the best achieving the highest accuracy in all 3 exercises,with an accuracy of 67% for the squats, 87% for the lunges and 75% for the hip bridges.Overall, the final model demonstrated promising capabilities of classifying exercisetechnique of basic lower-body exercises, with a real-time feedback implementationbeing a feasible solution for self-efficient fitness.

Considering that the working of mobile robots is facilitated by using distributed embedded systems capable of working cooperatively and that managing these systems requires distributed software applications generally written in C++ or Python, it can be argued that ROS (Robotics Operational Systems) can be considered as a useful tool in undertaking robotic projects as it facilitates the integration of software drivers for installed devices. With ROS, software devices can be kept separate but communicate data to achieve distributed control. This paper describes partial results of a currently ongoing research project, which employs a fuzzy controller to maintain a differential drive mobile robot in a path between crops rows, which are detected using LiDAR (Light Detection and Ranging) and IMU (Inertial Measurement Unit) sensors. Python programs are used to read sensors and control robot DC motors using a fuzzy controller working in a team on the ROS platform. This paper provides a general overview of our robot, including its structure, hardware, software, reached results and future works., Teniendo en cuenta que el funcionamiento de los robots móviles se ve facilitado por el uso de sistemas embebidos distribuidos capaces de trabajar de forma cooperativa y que la gestión de estos sistemas requiere aplicaciones de software distribuidas escritas generalmente en C++ o Python, se puede argumentar que ROS (Robotics Operational Systems) puede considerarse una herramienta útil a la hora de emprender proyectos robóticos, ya que facilita la integración de controladores de software para los dispositivos instalados. Con ROS, los dispositivos de software pueden mantenerse separados pero comunicar datos para lograr un control distribuido. Este artículo describe resultados parciales de un proyecto de investigación actualmente en curso, que emplea un controlador difuso para mantener un robot móvil de accionamiento diferencial en una trayectoria entre hileras de cultivos, que se detectan mediante sensores LiDAR (Light Detection and Ranging) e IMU (Inertial Measurement Unit). Se utilizan programas Python para leer los sensores y controlar los motores DC del robot utilizando un controlador difuso trabajando en equipo en la plataforma ROS. Este artículo proporciona una visión general de nuestro robot, incluyendo su estructura, hardware, software, resultados alcanzados y trabajos futuros.

The Swedish Transport Administration Research and Innovation fund for Maritime research funded the project "Reference data and algorithms to support research and development of smart ships". The project goes by the working name, and is communicated as, Reeds. It responds to a synthesis of a number of different needs identified in previous projects and studies. The background to the project is that in recent years the focus has been on developing algorithms to interpret and act on the physical environment around different types of craft. In order to be able to develop and evaluate these algorithms, it has become clear that open datasets and a fair benchmarking platform are required that allow various developers in industries and researchers to evaluate algorithms. In the road vehicle sector, Kitti, as of 2013, is the largest dataset used as a reference dataset. The dataset in this project contains sensor data from several data collection occasions within a maritime context, from high-precision sensors such as cameras, radar, lidar, and IMU. For marine applications, there has been no similar dataset with anywhere near the same amount of data and time synchronisation between sensors. The reference data and reference algorithms were available periodically during the project through an online service where researchers and developers could upload their algorithms to use the dataset. In addition to the dataset itself, Reeds adds additional strengths compared to other reference datasets: - New approach to comparing algorithms fairly, where new algorithms are always compared on a centralised hardware in a cloud service and re-evaluated when new data is added, i.e. an unbiased algorithm evaluation service. - Method that combines NTP and PTP time protocols for synchronisation between the sensors with microsecond accuracy - More types and more modern sensors that can be used at a higher level of abstraction and can thus be applied in more areas. - Se, Projektet “Referensdata och algoritmer till stöd för forskning och utveckling av smarta fartyg” har finansierats av Trafikverkets Forsknings - och Innovationsportfölj för sjöfart. Projektet går under arbetsnamnet och kommuniceras som Reeds svarar mot en syntes av ett antal olika behov som identifierats i tidigare projekt och studier. Bakgrunden till projektet är att de senaste åren har fokus lagts på att ta fram algoritmer för att tolka och agera på den fysiska miljön kring olika typer av farkoster. För att kunna utveckla och utvärdera dessa algoritmer har det blivit tydligt att det krävs öppna dataset och en rättvis benchmarkingplattform som tillåter olika utvecklare inom industrier och forskare att utvärdera algoritmer. Inom vägfordonssektorn är Kitti , från 2013, det största datasetet som används som referensdata set. Datasetet i detta projekt innehåller sensordata från flertalet datainsamlingstillfällen i en maritim kontext, från högprecisionssensorer som kameror, radar, lidar, och IMU. För maritima applikationer har det inte funnits något liknande dataset med tillnärmelsevis lika stor datamängd och med tidssynkronisering mellan sensorer. Referensdata och referensalgoritmerna var tillgängliga periodvis under projektet genom en onlinetjänst där forskare och utvecklare kunde ladda upp sina algoritmer för att använda datasetet. Utöver själva datasetet tillför Reeds ytterligare styrkor jämfört andra referensdata set: - Nytt tillvägagångssätt för att jämföra algoritmer rättvist, där nya algoritmer alltid jämförs på en centraliserad hårdvara i en molntjänst och omvärderas när nya data läggs till, dvs en opartisk tjänst för utvärdering av algoritmer. - Metod som kombinerar NTP och PTP tidsprotokoll för synkronisering mellan sensorerna med mikrosekunds noggrannhet - Fler typer och modernare sensorer som kan användas på en högre abstraktionsnivå, och kan därmed tillämpas inom fler områden. - Sensorfusion av både ombord sensorer och av sen, Projektet har finansierats av Trafikverkets Forskning och Innovations sjöfartsportfölj (P105354). Trafikverket projektnummer: TRV2019/120103, Referensdata och algoritmer till stöd för forskning och utveckling av smarta fartyg

Denna rapport presenterar en prototyp av en eldriven intelligent transportvagn som konstruerats vid ett projekt vid Institutionen för tillämpad fysik och elektronik på Umeå universitet. Projektet genomfördes i syfte att visa att det går att bygga en semi-autonom transportvagn som kan följa efter bakom och vid sidan av en person. I förlängningen är det tänkt att konceptet ska kunna användas för att effektivisera företag genom att möjliggöra automatisk transport av gods. För prototypen användes en eldriven minibil som utrustades med en Raspberry Pi 3, laser radar, och ett tröghetspaket. För komponenterna och minibilens motorstyrkort användes utvecklingsmiljön CODESYS för att utveckla och implementera drivrutiner och algoritmer. För att möjliggöra fjärrstyrning och kommunikation med transportvagnen skapades ett användargränssnitt i CODESYS. Resultatet är en eldriven transportvagn som kan söka och följa efter bakom och vid sidan av en person. I ett användargränssnitt kan transportvagnen fjärrstyras med funktioner som manuell styrning, växla mellan manuell och autonom styrning, växla mellan att följa bakom eller vid sidan av en person, utföra en transition där vagnen förflyttar sig från att vara bakom till vid sidan av en person, och kalibrera styrvinkel. I gränssnittet ses transportvagnens hastighet, styrvinkel, signallampor för kollision, avståndsinformation om personen som vagnen följer, data från tröghetspaketet, och en rutkarta över transportvagnens omgivning uppbyggt av laser radar-data. Konceptet har validerats via fyra testscenarion: Följa bakom en person inomhus, följa vid sidan av en person, transition från bakom till vid sidan av en person, och följa bakom en person utomhus. Projektet visar att det går att bygga en prototyp på en eldriven intelligent transportvagn som kan följa en person både inomhus och utomhus, och att det är ett tillfälle för företag att investera i intelligenta drivlinor., This report presents a prototype of an electric intelligent transport carrier designed in a project at the Department of Applied Physics and Electronics at Umeå University. The project was carried out with the aim of demonstrating that it is possible to build a semi-autonomous transport carrier that can follow behind and next to a person. In extension, it is intended that the concept should be able to be used to streamline companies by enabling automatic transport of goods. The prototype used an electric minivan equipped with a Raspberry Pi 3, laser radar, and an Inertial Measurement Unit. Drivers and algorithms were developed and implemented in the CODESYS development environment. A user interface was created in CODESYS to enable remote control and communication with the carrier. The result is an electric carrier that can track and follow behind and next to a person. In a user interface, the carrier can be remotely controlled with functions such as manual control, switching between manual and autonomous control, swapping between following behind or next to a person, performing a transition in which the car moves from being behind to next to a person, and calibration of steering angle. The interface displays the carrier's speed, steering angle, signaling lamps for collision, distance information about the person the carrier is following, data from the Inertial Measurement Unit, and a 2D-GridMap of the carrier’s environment based on laser radar data. The concept has been validated through four test scenarios: Follow behind a person indoors, follow next to a person, transition from behind to next to a person, and follow behind a person outdoors. The project shows that it is possible to build a prototype of an electric intelligent transport carrier that can follow a person both indoors and outdoors, and that it is an opportunity for companies to invest in intelligent carriers.

Denna rapport presenterar en prototyp av en eldriven intelligent transportvagn som konstruerats vid ett projekt vid Institutionen för tillämpad fysik och elektronik på Umeå universitet. Projektet genomfördes i syfte att visa att det går att bygga en semi-autonom transportvagn som kan följa efter bakom och vid sidan av en person. I förlängningen är det tänkt att konceptet ska kunna användas för att effektivisera företag genom att möjliggöra automatisk transport av gods. För prototypen användes en eldriven minibil som utrustades med en Raspberry Pi 3, laser radar, och ett tröghetspaket. För komponenterna och minibilens motorstyrkort användes utvecklingsmiljön CODESYS för att utveckla och implementera drivrutiner och algoritmer. För att möjliggöra fjärrstyrning och kommunikation med transportvagnen skapades ett användargränssnitt i CODESYS. Resultatet är en eldriven transportvagn som kan söka och följa efter bakom och vid sidan av en person. I ett användargränssnitt kan transportvagnen fjärrstyras med funktioner som manuell styrning, växla mellan manuell och autonom styrning, växla mellan att följa bakom eller vid sidan av en person, utföra en transition där vagnen förflyttar sig från att vara bakom till vid sidan av en person, och kalibrera styrvinkel. I gränssnittet ses transportvagnens hastighet, styrvinkel, signallampor för kollision, avståndsinformation om personen som vagnen följer, data från tröghetspaketet, och en rutkarta över transportvagnens omgivning uppbyggt av laser radar-data. Konceptet har validerats via fyra testscenarion: Följa bakom en person inomhus, följa vid sidan av en person, transition från bakom till vid sidan av en person, och följa bakom en person utomhus. Projektet visar att det går att bygga en prototyp på en eldriven intelligent transportvagn som kan följa en person både inomhus och utomhus, och att det är ett tillfälle för företag att investera i intelligenta drivlinor., This report presents a prototype of an electric intelligent transport carrier designed in a project at the Department of Applied Physics and Electronics at Umeå University. The project was carried out with the aim of demonstrating that it is possible to build a semi-autonomous transport carrier that can follow behind and next to a person. In extension, it is intended that the concept should be able to be used to streamline companies by enabling automatic transport of goods. The prototype used an electric minivan equipped with a Raspberry Pi 3, laser radar, and an Inertial Measurement Unit. Drivers and algorithms were developed and implemented in the CODESYS development environment. A user interface was created in CODESYS to enable remote control and communication with the carrier. The result is an electric carrier that can track and follow behind and next to a person. In a user interface, the carrier can be remotely controlled with functions such as manual control, switching between manual and autonomous control, swapping between following behind or next to a person, performing a transition in which the car moves from being behind to next to a person, and calibration of steering angle. The interface displays the carrier's speed, steering angle, signaling lamps for collision, distance information about the person the carrier is following, data from the Inertial Measurement Unit, and a 2D-GridMap of the carrier’s environment based on laser radar data. The concept has been validated through four test scenarios: Follow behind a person indoors, follow next to a person, transition from behind to next to a person, and follow behind a person outdoors. The project shows that it is possible to build a prototype of an electric intelligent transport carrier that can follow a person both indoors and outdoors, and that it is an opportunity for companies to invest in intelligent carriers.

Short stick exercises have been attracting attention from the viewpoint of preventing falls and improving the health of elderly people and are generally performed under the guidance of instructors and nursing staff at nursing homes. However, in situations such as the COVID-19 pandemic, where people should refrain from unnecessary outings, it is advisable that individuals perform short stick exercises at home and record their exercise implementation status. In this paper, we propose an inertial measurement unit (IMU)-sensor-based short stick exercise tracking method that can automatically record the types and amounts of exercises performed using a short stick equipped with an IMU sensor. The proposed method extracts time-domain and frequency-domain features from linear acceleration and quaternion time-series data obtained from the IMU sensor and classifies the type of exercise using an inference model based on machine learning algorithms. To evaluate the proposed method, we collected sensor data from 21 young subjects (in their 20s) and 14 elderly subjects (79–95 years old), where the participants performed three sets (10 times per set) of eight basic types of short stick exercises (five types for elderly people). As a result of evaluating the proposed method using this data set, we confirmed that when LightGBM was used as the learning algorithm, it achieved F values of 90.0 and 86.6% for recognizing the type of exercise for young and elderly people, respectively.

Syftet med denna avhandling är att utveckla en ny metod för att producera bilder med syntetisk aperturradar (SAR), med utgångspunkt i scenarier med arbiträr rörelse vad gäller radarsensorn. SAR är en väletablerad metod för att skapa 2- eller 3-dimensionella radarbilder, som traditionellt sett antar att radar-sensorns rörelse är linjär och förutsägbar. Därmed är positionerna för varje radardata-punkt kända och följer ett fördefinierat mönster. Denna avhandling utforskar emellertid nya metoder som gör det möjligt för radarn att röra sig fritt. Vi har utvecklat en prototyp baserad på Pulsed Coherent Radar-sensorn A121 från Acconeer för att demonstrera potentialen och prestandan i vår metod. Det resulterande systemet kan framgångsrikt skapa en bild av sin omgivning och upptäcka hinder i 3D-rymden. Med hjälp av denna prototypuppställning har vi utvärderat olika konfigurationer och optimeringar av algoritmen, samt konfigurationer för radarsensorn, inklusive val av lins. Vi har testat uppställningen för många olika potentiella objekt och utvärderat dess prestanda i flera olika miljöer. Systemet förlitar sig på en ström av exakta poseringsdata för radar-sensorn. Två olika system utforskades för detta ändamål. Först en Inertial Measurement Unit (IMU), och sedan VR-hårdvara som bygger på IR-fyr-spårning. Tidiga experiment visade mindre lovande resultat för IMU-baserad spårning, och denna väg ansågs inte vara värd att utforska vidare. Att spåra sensorn med hjälp av en VR-uppsättning visade emellertid lovande resultat, och fler experiment genomfördes för att utvärdera användbarheten och begränsningarna i dess användning för att skapa radarbilder. Dessa två tillvägagångssätt är dock en delmängd av ett stort antal potentiella spårningssystem. Våra resultat visar att SAR är möjligt att implementera i fall där radarns rörelse är arbiträr, förutsatt att sensorns position kan bestämmas med viss noggrannhet för alla insamlade radardatapunkter. Det visar också hur SAR baserad på arbitr, The purpose of this thesis is to develop a novel approach to producing Synthetic Aperture Radar (SAR) images, assuming the scenario of arbitrary motion in regards to the radar sensor. SAR is a well-researched method for creating 2- or 3-dimensional radar images, traditionally assuming the radar sensor's motion to be linear and highly predictable. Thus the locations where each radar data is sampled are known and follow a predefined pattern. This thesis, however, explores new methods that enable the radar to move freely. We have developed a prototype based on the A121 Pulsed Coherent Radar sensor from Acconeer to demonstrate the performance and use-case potential of the method we came up with. The resulting system can successfully create an accurate map of its surroundings, detecting obstacles in 3D space. Using this prototype setup, we have evaluated different configurations and kinds of optimizations for the algorithm developed, as well as configurations for the radar sensor, including lens choice. We have tested the setup for many different potential targets and evaluated its performance in several different environments. The system relies on a stream of accurate pose data in regard to the radar sensor. Two different systems were explored for this purpose. Firstly, the use of an Inertial Measurement Unit (IMU), and secondly, using the IR lighthouse tracking from virtual reality hardware. Early experiments showed less than promising results for IMU-based tracking, and this path was not deemed worth exploring further. Tracking the sensor using a VR setup, however, showed promising results and more experiments were performed to evaluate the viability and limitations of its use in creating radar images. These two approaches, though, are a subset of a vast number of potential pose-tracking systems. The final results show that SAR is possible to implement in cases where the motion of the radar is arbitrary, given the position of the sensor can be determined to some degre, Radar is a technology that uses radio waves to detect and locate objects in its vicinity. In its most simple form, it works by emitting a radio wave signal and measuring the time it takes for the signal to bounce back after it has hit an object, much like how sonar works with sound. This information is then used to determine the distance, speed, and other characteristics of the object. More sophisticated radar technologies, though, have a wide range of use cases, from military applications to weather forecasting and air traffic control. In military applications, radar is used for surveillance, target acquisition, and weapon guidance. Weather radar is used to track and forecast storms, while air traffic control radar tracks the location and speed of aircraft in flight. Acconeer AB, a Malmö-based company started in 2012 around research into Pulsed Coherent Radar (PCR) technology at LTH, develops a now commercially available 60GHz radar sensor with a small footprint, approachable price, and small power consumption. Its A111 and A121 radar sensors are seeing increasing adoption by a plethora of tech giants and independent inventors alike for a multitude of use cases. Recently, Volvo announced Acconeer radar-based interior presence detection will be featured in some upcoming car models. Consumer electronic use cases like this are a very novel concept, just springing up in the last decade, and there is yet a lot to explore in this field. In recent years, there have been several modern developments in radar technology. One of these is the use of Synthetic Aperture Radar (SAR) which, for example, allows for the creation of high-resolution images of the Earth's surface by using a radar system on a moving platform, such as a satellite or airplane. SAR is a type of radar that uses the motion of the radar antenna to create images containing a lot of information that would not be possible to achieve with a single radar sweep. The antenna motion allows the radar to gather data fr

Can we reduce disturbance, swing, and errors by delegating some of the tasks to a subsystem on the tool hanging from a drone to gain precision and dependability? This project covers developing and implementing a user-controlled prototype with an automatic control system and gripper for functionality and controlled movement in 5 DOF. AirForestry is developing a new way of working in the forestry business to provide a greener and healthier solution for the soil and the environment. The forestry industry emits approximately one million tons of CO2 per year from heavy diesel-fueled vehicles. With a renewable energy battery-powered drone of a diameter of 6.2 m and a hanging harvesting tool, they can significantly improve the environmental impact of forestry. The harvesting tool has an automatic control system and is user controlled to perform the thinning and cutting of trees. The main focus is to use IMU sensor data to produce accurate angle estimation to control the stability of a scaled prototype and to implement physical restrictions to make it able to be attached safely and tested on a drone but not limit the controlled mobility of the prototype with a system independent of the control system of the drone itself. The user can control the attitude and elevation and receive real-time sensor data wireless during operation. With the intended spacing of the attachment points on the drone, it can prevent unwanted swing from a push or displaced drop. It uses the accelerometer on the IMUsensor to calculate the angle of roll and pitch, although the accelerometer is sensitive to vibrations and rotations. The control system has a fast response and rise time, but it experiences noise and oscillations. Sensor fusion of the accelerometer and gyroscope in a complementary filter can be implemented to increase the accuracy of the angle estimations and decrease the noise, which will be reflected in the speed of the servos and thereby improve the stability and mobility of the system.

Can we reduce disturbance, swing, and errors by delegating some of the tasks to a subsystem on the tool hanging from a drone to gain precision and dependability? This project covers developing and implementing a user-controlled prototype with an automatic control system and gripper for functionality and controlled movement in 5 DOF. AirForestry is developing a new way of working in the forestry business to provide a greener and healthier solution for the soil and the environment. The forestry industry emits approximately one million tons of CO2 per year from heavy diesel-fueled vehicles. With a renewable energy battery-powered drone of a diameter of 6.2 m and a hanging harvesting tool, they can significantly improve the environmental impact of forestry. The harvesting tool has an automatic control system and is user controlled to perform the thinning and cutting of trees. The main focus is to use IMU sensor data to produce accurate angle estimation to control the stability of a scaled prototype and to implement physical restrictions to make it able to be attached safely and tested on a drone but not limit the controlled mobility of the prototype with a system independent of the control system of the drone itself. The user can control the attitude and elevation and receive real-time sensor data wireless during operation. With the intended spacing of the attachment points on the drone, it can prevent unwanted swing from a push or displaced drop. It uses the accelerometer on the IMUsensor to calculate the angle of roll and pitch, although the accelerometer is sensitive to vibrations and rotations. The control system has a fast response and rise time, but it experiences noise and oscillations. Sensor fusion of the accelerometer and gyroscope in a complementary filter can be implemented to increase the accuracy of the angle estimations and decrease the noise, which will be reflected in the speed of the servos and thereby improve the stability and mobility of the system.

Godkänd;2023;Nivå 0;2023-05-09 (hanlid)

A three-dimensional motion capture system (MoCap) and the Garmin Running Dynamics Pod can be utilised to monitor a variety of dynamic parameters during running. The present investigation was designed to examine the validity of these two systems for determining ground contact times while running in place by comparing the values obtained with those provided by the bilateral force plate (gold standard). Eleven subjects completed three 20-s runs in place at self-selected rates, starting slowly, continuing at an intermediate pace, and finishing rapidly. The ground contact times obtained with both systems differed significantly from the gold standard at all three rates, as well as for all the rates combined (p < 0.001 in all cases), with the smallest mean bias at the fastest step rate for both (11.5 ± 14.4 ms for MoCap and −81.5 ± 18.4 ms for Garmin). This algorithm was developed for the determination of ground contact times during normal running and was adapted here for the assessment of running in place by the MoCap, which could be one explanation for its lack of validity. In conclusion, the wearables developed for monitoring normal running cannot be assumed to be suitable for determining ground contact times while running in place., Validerad;2023;Nivå 2;2023-09-07 (joosat);Funder: Slovenian Research Agency (grant number P5-0147);CC BY 4.0 License