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The purpose of this paper is to describe ongoing research on appropriate instrumentation and analysis techniques to characterize postural stability, postural agility, and dynamic stability, which collectively comprise the postural control spectrum. This study had a specific focus on using emerging sensors to develop protocols suitable for use outside laboratory or clinical settings. First, we examined the optimal number and placement of wearable accelerometers for assessing postural stability. Next, we proposed metrics and protocols for assessing postural agility with the use of a custom force plate-controlled video game. Finally, we proposed a method to quantify dynamic stability during walking tasks using novel frequency-domain metrics extracted from acceleration data obtained with a single body-worn IMU. In each of the three studies, a surrogate for instability was introduced, and the sensors and metrics discussed in this paper show promise for differentiating these trials from stable condition trials. Next steps for this work include expanding the tested population size and refining the methods to even more reliably and unobtrusively characterize postural control status in a variety of scenarios. [ABSTRACT FROM AUTHOR]

Over the last three decades, satellite imagery has been instrumental in mapping and monitoring water quality. However, satellites often have limitations due to image availability and cloud cover. Today, the spatial resolution of satellite images does not provide finer detail measurements essential for small‐scale water pollution management. Drones offer a complimentary platform capable of operating below cloud cover and acquiring very high spatial resolution datasets in near real‐time. Studies have shown that drone mapping over water can be done via the Direct Georeferencing approach. However, this method is only suitable for high‐end drones with accurate GNSS/IMU. Importantly, this limitation is exacerbated because of the difficulty in placing targets over water, which can be used to improve the accuracy after the survey. This study explored a new method called Assisted Direct Georeferencing which combines the benefits of traditional Bundle Adjustment with Direct Georeferencing. The performance of the approach was evaluated over a variety of different scenarios, demonstrating significant improvement in the planimetric accuracy. From the results, the method reduced the error in XY of drone imagery from MAE of 18.9 to 3.4 m. The result shows the potential of low‐cost drones with Assisted Direct Georeferencing in closing the gap to high‐end drones. [ABSTRACT FROM AUTHOR]

Camptocormia, a severe flexion deformity of the spine, presents challenges in monitoring its progression outside laboratory settings. This study introduces a customized method utilizing four inertial measurement unit (IMU) sensors for continuous recording of the camptocormia angle (CA), incorporating both the consensual malleolus and perpendicular assessment methods. The setup is wearable and mobile and allows measurements outside the laboratory environment. The practicality for measuring CA across various activities is evaluated for both the malleolus and perpendicular method in a mimicked Parkinson disease posture. Multiple activities are performed by a healthy volunteer. Measurements are compared against a camera-based reference system. Results show an overall root mean squared error (RMSE) of 4.13° for the malleolus method and 2.71° for the perpendicular method. Furthermore, patient-specific calibration during the standing still with forward lean activity significantly reduced the RMSE to 2.45° and 1.68° respectively. This study presents a novel approach to continuous CA monitoring outside the laboratory setting. The proposed system is suitable as a tool for monitoring the progression of camptocormia and for the first time implements the malleolus method with IMU. It holds promise for effectively monitoring camptocormia at home. [ABSTRACT FROM AUTHOR]

Manual wheelchair users (MWUs) frequently report shoulder problems and have a three-times-higher likelihood of rotator cuff pathology compared to able-bodied individuals. Shoulder health is crucial for MWU independence, their social participation, and quality of life. Daily activities such as wheelchair propulsion potentially lead to fatigue and overload. Since comprehensive data are limited, this study aimed to implement a wheelchair mobility metrics (WCMM) method to examine various aspects of wheelchair use in daily life. Two inertial measurement units (IMUs) were placed on the wheelchair frame and wheel of 19 participants with a spinal cord injury (SCI). WCMMs like distance covered, number of pushes and turns, and incline were derived from real-life measurements and normalized to a period of 8 h. Large variation was observed among participants. The distance covered ranged from 0.5 to 10.7 km, with the number of pushes from 438 to 4820. The number of turns ranged from 269 to 1396, and the average distance per mobility bout from 5 to 59 m. This wide variation over participants emphasizes the importance of data-driven clinical decision making and patient education. Further studies with larger samples and duration are needed to fully understand MWUs' mobility patterns and their implications for shoulder health. [ABSTRACT FROM AUTHOR]

The objective of this research was to validate a single, trunk-mounted wearable sensor (Optimeye S5, Catapult Australia, Melbourne) to measure the cadence of swimming strokes, cycling pedals and running strides in a triathlon. While similar validations have been performed in swimming and running, it is a novel application in cycling, and thus, across a whole triathlon. Seven triathletes were recruited to participate in a sprint distance triathlon which was filmed and simultaneously measured by a single, trunk-mounted wearable sensor. To validate the wearable sensor, individual swimming strokes, cycling pedal strokes and running strides were manually counted by viewing the wearable sensor data and video footage. While analysing cycling data, changes in cycling subtask performances were noticed, thus, a secondary analysis in cycling was conducted to investigate. The 95% limits of agreement analysis indicated the sensor validly measured swimming strokes (mean bias = −0.034 strokes), cycling pedal strokes (mean bias = −0.09 strokes) and running strides (mean bias = 0.00 strides) with minimal to no bias (p > 0.05). Further analysis of cycling revealed the wearable sensor is an acceptably valid tool to measure the duration of out of saddle riding (mean bias = 0.08 s), however, significant differences in the duration of in saddle riding (mean bias = −0.5 s) and coasting were identified (mean bias = 0.39 s). A single trunk mounted wearable sensor is a valid tool to measure movement cadence in a triathlon, however, further validation is required to generate a full understanding of cycling subtask performances. [ABSTRACT FROM AUTHOR]

Although there are numerous locomotion studies analyzing the degree of attenuation of the acceleration spikes in the lower limbs and the trunk, few of these studies relate to tennis, where a high percentage of injuries occur in these body segments. The aim of this study was to describe the acceleration spikes and the attenuation response along the trunk, in real game actions. For this purpose, accelerometers were placed on the lower trunk, the upper trunk, and the head on a sample of 19 players while playing tennis matches. An average of 530 ± 146 acceleration spikes per match were selected in the upper trunk and a clear attenuation response between the upper trunk and the head was found (acceleration spike magnitude was approximately 25 m/s2 in the upper trunk and approximately 20 m/s2 in the head; p < 0.05; with attenuation percentages above 15%). In all players acceleration spikes of the head were below lower and upper trunk acceleration (p < 0.05 in all repeated measures ANOVAs and effect sizes were above 0.8, or large effect sizes). However, between the lower trunk and upper trunk no clear attenuation was found and although in some players the impact peaks were higher in the lower trunk (p < 0.05) the effect sizes were negligible or medium (Cohen d < 0.5). In other players the upper trunk peaks were higher than the lower trunk peaks (p < 0.05) and in a few players there was no significant difference (p > 0.05). The attenuation in the upper trunk, probably serves as a head protection/stabilization mechanism and more studies are needed to analyze the biomechanics actions underlying this attenuation response. [ABSTRACT FROM AUTHOR]

We introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson's disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson's disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects' age and motor impairments highlights the versatility of our approach. [ABSTRACT FROM AUTHOR]

Snake robots require autonomous localization and mapping capabilities for field applications. However, the characteristics of their motion, such as large turning angles and fast rotation speeds, can lead to issues like drift or even failure in positioning and map building. In response to this situation, this paper starts from the gait motion characteristics of the snake robot itself, proposing an improved gait motion method and a tightly coupled method based on IMU and visual information to solve the problem of poor algorithm convergence caused by head-shaking in snake robot SLAM. Firstly, the adaptability of several typical gaits of the snake robot to SLAM methods was evaluated. Secondly, the serpentine gait was selected as the object of gait improvement, and a head stability control method for the snake robot was proposed, thereby reducing the interference of the snake robot's motion on the sensors. Thirdly, a visual–inertial tightly coupled SLAM method for the snake robot's serpentine gait and Arc-Rolling gait was proposed, and the method was verified to enhance the robustness of the visual SLAM algorithm and improve the positioning and mapping accuracy of the snake robot. Finally, experiments proved that the methods proposed in this paper can effectively improve the accuracy of positioning and map building for snake robots. [ABSTRACT FROM AUTHOR]

Purpose: To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results. Design/methodology/approach: The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map's poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification. Findings: A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments. Originality/value: This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy. [ABSTRACT FROM AUTHOR]

Step counting devices were previously shown to be efficient in a variety of applications such as athletic training or patient's care programs. Various sensor placements and algorithms were previously experimented, with a best mean absolute percentage error (MAPE) close to 1% in simple mono-activity walking conditions. In this study, an existing running shoe was first instrumented with an inertial measurement unit (IMU) and used in the context of multi-activity trials, at various speeds, and including several transition phases. A total of 21 participants with diverse profiles (gender, age, BMI, activity style) completed the trial. The data recorded was used to develop a step counting algorithm based on a deep learning approach, and further validated against a k-fold cross validation process. The results revealed that the step counts were highly correlated to gyroscopes and accelerometers norms, and secondarily to vertical acceleration. Reducing input data to only those three vectors showed a very small decrease in the prediction performance. After the fine-tuning of the algorithm, a MAPE of 0.75% was obtained. Our results show that such very high performances can be expected even in multi-activity conditions and with low computational resource needs making this approach suitable for embedded devices. [ABSTRACT FROM AUTHOR]

Automatic detection of foot lift is one of the most important events of Functional Electrical Stimulation (FES). The FES system is used for the correction of Foot Drop (FD). FD is a condition where a person is unable to lift their foot from the ground due to complications that may arise after a stroke or spinal cord injury. It is crucial to accurately detect the patient's foot lift event when correcting FD through FES as the pulse should only be applied when the person lifts their foot. The FES system applies the electrical pulse based on the input of the foot-lift detection sensor. A conventional FES system employs a sensor that is affixed on the heel to detect the lifting of the foot, but the connecting cables make the patient uncomfortable. To address this problem, IMU (Inertial Measurement Unit)-based sensors have been used, but they have some disadvantages, such as false triggering, low accuracy, and calibration. In this paper, we have presented an algorithm for detecting foot-lift events with high accuracy using a single IMU sensor through the application of deep learning techniques. We have recorded data from 10 healthy people and 10 foot drop patients. We have implemented Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Convolutional Neural Network (CNN) models on these data and compared the results of these three models. The proposed algorithm aims to improve the precision of stimulation in the FES system. [ABSTRACT FROM AUTHOR]

Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo camera and two inertial measurement units (IMUs) to capture cargo motion in five degrees of freedom (DOF). By merging data from the stereo camera and IMUs, the system accurately determines the cargo's position and attitude relative to the camera. The specific methodology is introduced as follows: First, the YOLO model is adopted to identify targets in the image and generate bounding boxes. Then, using the principle of binocular disparity, the depth within the bounding box is calculated to determine the target's three-dimensional position in the camera coordinate system. Simultaneously, the IMU measures the attitude of the cargo, and a Kalman filter is applied to fuse the data from the two sensors. Experimental results indicate that the system's measurement errors in the x, y, and z directions are less than 2.58%, 3.35%, and 3.37%, respectively, while errors in the roll and pitch directions are 3.87% and 5.02%. These results demonstrate that the designed measurement system effectively provides the necessary motion information in 5-DOF for vessel-mounted crane control, offering new approaches for pose detection of marine cranes and cargoes. [ABSTRACT FROM AUTHOR]

Inertial Measurement Units are widely used in various applications and, hardware-wise, they primarily consist of a tri-axial accelerometer and a tri-axial gyroscope. For low-end commercial employments, the low cost of the device is crucial: this makes MEMS-based sensors a popular choice in this context. However, MEMS-based transducers are prone to significant, non-uniform and environmental-condition-dependent systematic errors, that require frequent re-calibration to be eliminated. To this end, identification methods that can be performed in-field by non-expert users, without the need for high-precision or costly equipment, are of particular interest. In this paper, we propose an in-field identification procedure based on the Total Least Squares method for both tri-axial accelerometers and gyroscopes. The proposed identification model is linear and requires no prior knowledge of the parameters to be identified. It enables accelerometer calibration without the need for specific reference surface orientation relative to Earth's gravity and allows gyroscope calibration to be performed independently of accelerometer data, without requiring the sensor's sensitive axes to be aligned with the rotation axes during calibration. Experiments conducted on NXP sensors FXOS8700CQ and FXAS21002 demonstrated that using parameters identified by our method reduced cross-validation standard deviations by about two orders of magnitude compared to those obtained using manufacturer-provided parameters. This result indicates that our method enables the effective calibration of IMU sensor parameters, relying only on simple 3D-printed equipment and significantly improving IMU performance at minimal cost. [ABSTRACT FROM AUTHOR]

AbstractWearable technologies such as inertial measurement units (IMUs) can be used to evaluate human gait and improve mobility, but sensor fixation is still a limitation that needs to be addressed. Therefore, aim of this study was to create a machine learning algorithm to predict gait events using a single IMU mimicking the carrying of a smartphone. Fifty-two healthy adults (35 males/17 females) walked on a treadmill at various speeds while carrying a surrogate smartphone in the right hand, front right trouser pocket, and right jacket pocket. Ground-truth gait events (e.g. heel strikes and toe-offs) were determined bilaterally using a gold standard optical motion capture system. The tri-dimensional accelerometer and gyroscope data were segmented in 20-ms windows, which were labelled as containing or not the gait events. A long-short term memory neural network (LSTM-NN) was used to classify the 20-ms windows as containing the heel strike or toe-off for the right or left legs, using 80% of the data for training and 20% of the data for testing. The results demonstrated an overall accuracy of 92% across all phone positions and walking speeds, with a slightly higher accuracy for the right-side predictions (∼94%) when compared to the left side (∼91%). Moreover, we found a median time error <3% of the gait cycle duration across all speeds and positions (∼77 ms). Our results represent a promising first step towards using smartphones for remote gait analysis without requiring IMU fixation, but further research is needed to enhance generalizability and explore real-world deployment. [ABSTRACT FROM AUTHOR]

Introduction: The shift of the horse breeding sector from agricultural to leisure and sports purposes led to a decrease in local breeds’ population size due to the loss of their original breeding purposes. Most of the Italian breeds must adapt to modern market demands, and gait traits are suitable phenotypes to help this process. Inertial measurement unit (IMU) technology can be used to objectively assess them. This work aims to investigate on IMU recorded data (i) the influence of environmental factors and biometric measurements, (ii) their repeatability, (iii) the correlation with judge evaluations, and (iv) their predictive value. Material and methods: The Equisense Motion S® was used to collect phenotypes on 135 horses, Bardigiano (101) and Murgese (34) and the data analysis was conducted using R (v.4.1.2). Analysis of variance (ANOVA) was employed to assess the effects of biometric measurements and environmental and animal factors on the traits. Results and discussion: Variations in several traits depending on the breed were identified, highlighting different abilities among Bardigiano and Murgese horses. Repeatability of horse performance was assessed on a subset of horses, with regularity and elevation at walk being the traits with the highest repeatability (0.63 and 0.72). The positive correlation between judge evaluations and sensor data indicates judges’ ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). A high variability was observed in the accuracy of the SVM model, ranging from 55 to 100% while the other two models showed higher consistency, with accuracy ranging from 74 to 100% for the GBM and from 64 to 88% for the KNN. Overall, the GBM model exhibits the highest accuracy and the lowest error. In conclusion, integrating IMU technology into horse performance evaluation offers valuable insights, with implications for breeding and training. [ABSTRACT FROM AUTHOR]

Introduction: Motion blur, primarily caused by rapid camera movements, significantly challenges the robustness of feature point tracking in visual odometry (VO). Methods: This paper introduces a robust and efficient approach for motion blur detection and recovery in blur-prone environments (e.g., with rapid movements and uneven terrains). Notably, the Inertial Measurement Unit (IMU) is utilized for motion blur detection, followed by a blur selection and restoration strategy within the motion frame sequence. It marks a substantial improvement over traditional visual methods (typically slow and less effective, falling short in meeting VO's realtime performance demands). To address the scarcity of datasets catering to the image blurring challenge in VO, we also present the BlurVO dataset. This publicly available dataset is richly annotated and encompasses diverse blurred scenes, providing an ideal environment for motion blur evaluation. [ABSTRACT FROM AUTHOR]

Background: Gait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia. Methods: Six electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included. Results: A total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia. Conclusions: IMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems. [ABSTRACT FROM AUTHOR]

The use of inertial measurement units (IMUs) as an alternative to optical marker-based systems has the potential to make gait analysis part of the clinical standard of care. Previously, an IMU-based system leveraging Rauch–Tung–Striebel smoothing to estimate knee angles was assessed using a six-degrees-of-freedom joint simulator. In a clinical setting, however, accurately measuring abduction/adduction and external/internal rotation of the knee joint is particularly challenging, especially in the presence of soft tissue artefacts. In this study, the in vivo IMU-based joint angles of 40 asymptomatic knees were assessed during level walking, under two distinct sensor placement configurations: (1) IMUs fixed to a rigid harness, and (2) IMUs mounted on the skin using elastic hook-and-loop bands (from here on referred to as "skin-mounted IMUs"). Estimates were compared against values obtained from a harness-mounted optical marker-based system. The comparison of these three sets of kinematic signals (IMUs on harness, IMUs on skin, and optical markers on harness) was performed before and after implementation of a REference FRame Alignment MEthod (REFRAME) to account for the effects of differences in coordinate system orientations. Prior to the implementation of REFRAME, in comparison to optical estimates, skin-mounted IMU-based angles displayed mean root-mean-square errors (RMSEs) up to 6.5°, while mean RMSEs for angles based on harness-mounted IMUs peaked at 5.1°. After REFRAME implementation, peak mean RMSEs were reduced to 4.1°, and 1.5°, respectively. The negligible differences between harness-mounted IMUs and the optical system after REFRAME revealed that the IMU-based system was capable of capturing the same underlying motion pattern as the optical reference. In contrast, obvious differences between the skin-mounted IMUs and the optical reference indicated that the use of a harness led to fundamentally different joint motion being measured, even after accounting for reference frame misalignments. Fluctuations in the kinematic signals associated with harness use suggested the rigid device oscillated upon heel strike, likely due to inertial effects from its additional mass. Our study proposes that optical systems can be successfully replaced by more cost-effective IMUs with similar accuracy, but further investigation (especially in vivo and upon heel strike) against moving videofluoroscopy is recommended. [ABSTRACT FROM AUTHOR]

In its chronic and non-specific form, low back pain is experienced by a large percentage of the population; its persistence impacts the quality of life and increases costs to the health care system. In recent years, the scientific literature highlights how treatment based on assessment and functional recovery is effective through IMU technology with biofeedback or exergaming as part of the tools available to assist the evaluation and treatment of these patients, who present not only with symptoms affecting the lumbar spine but often also incorrect postural attitudes. Aim: Evaluate the impact of technology, based on inertial sensors with biofeedback or exergaming, in patients with chronic non-specific low back pain. A systematic review of clinical studies obtained from PubMed, Scopus, Science Direct, and Web of Science databases from 1 January 2016 to 1 July 2024 was conducted, developing the search string based on keywords and combinations of terms with Boolean AND/OR operators; on the retrieved articles were applied inclusion and exclusion criteria. The procedure of publication selection will be represented with the PRISMA diagram, the risk of bias through the RoB scale 2, and methodological validity with the PEDro scale. Eleven articles were included, all RCTs, and most of the publications use technology with exergaming within about 1–2 months. Of the outcomes measured, improvements were reported in pain, disability, and increased function; the neuropsychological sphere related to experiencing the pathology underwent improvements. From the results obtained, the efficacy of using technology based on exergames and inertial sensors, in patients with chronic non-specific low back pain, was increased. Further clinical studies are required to achieve more uniformity in the proposed treatment to create a common guideline for health care providers. [ABSTRACT FROM AUTHOR]

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person's psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications. [ABSTRACT FROM AUTHOR]

Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs. [ABSTRACT FROM AUTHOR]

Rowing is a complex sport where technique can significantly impact performance. A better understanding of the rowers' technique and neuromuscular activations during scull rowing, along with their impact on rowing performance, could greatly help trainers and athletes. Twelve male rowers were asked to row at their competitive stroke rate, and we collected data describing neuromuscular activations, trunk and arm kinematics, as well as technical determinants such as oar angles and angle asymmetries. We fitted linear mixed-effect models to investigate the effects of these variables on power production and boat speed. A larger effective angle had the greatest positive effect on power output, and slip angles had the largest negative effects. Increased elbow flexion at catch had the greatest negative effect on speed. Angle asymmetries affected neither power nor speed. Increased upper limb neuromuscular activity during the first and third quarters of the drive phase helped reduce slip angles, thus increasing performance. Power and speed were influenced similarly by the predictor variables. Still, they showed subtle differences, indicating that the strategies to maximize power production might not be the best ones to also achieve the maximum speed. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in underwater navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL), hindering the development of low-cost vehicles. Micro Electro Mechanical System Inertial Measurement Units (MEMS IMUs) are widely used in industry due to their low cost and can output acceleration and angular velocity, making them suitable as an Attitude Heading Reference System (AHRS) for low-cost vehicles. However, poorly calibrated MEMS IMUs provide an inaccurate angular velocity, leading to rapid drift in orientation. In underwater environments where AUVs cannot use GPS for position correction, this drift can have severe consequences. To address this issue, this paper proposes Underwater Gyros Denoising Net (UGDN), a method based on dilated convolutions and LSTM that learns and extracts the spatiotemporal features of IMU sequences to dynamically compensate for the gyroscope's angular velocity measurements, reducing attitude and heading errors. In the experimental section of this paper, we deployed this method on a dataset collected from field trials and achieved significant results. The experimental results show that the accuracy of MEMS IMU data denoised by UGDN approaches that of fiber-optic SINS, and when integrated with DVL, it can serve as a low-cost underwater navigation solution. [ABSTRACT FROM AUTHOR]

As the natural conclusion of talent identification in sports, talent development is the process that involves improving biomechanical capacities and bio-motor abilities. The development progress can be objectively assessed and monitored through measurements of trainability. This study introduces a practical methodology to assess motor control as a trainable factor using kinematic data. The study focused on establishing the relationship between kinematic data and changes in muscle strength and dynamic balance. It illustrates how wearable technology can assess trainability during a functional training programme. Twenty-six female university students were selected and divided into intervention and control groups to investigate motor control trainability. The intervention group performed step aerobics exercises for 24 sessions. A single inertial measurement unit (IMU) mounted on S1 captured the oscillatory motion profiles of the centre of mass during these rhythmic exercises. Analysis revealed that the amplitude of linear jerk variability in different anatomical planes could reflect core and lower limb muscle strengthening caused by training. Furthermore, the results indicated that the dynamic balance adaptation to the changing tempo throughout the training programme was dictated primarily by step width. The mediolateral linear jerk variability reflected this adaptation. The minimum instrumentation approach proposed by this study could prove very practical for the talent development monitoring. The methodology illustrates how the recorded kinematic data from an appropriately placed single IMU could become an information-rich source for the coach to monitor, assess and quantify the trainee's progress during long-term athletic development. [ABSTRACT FROM AUTHOR]

Abstract Background Gait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia. Methods Six electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included. Results A total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia. Conclusions IMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems.

Inertial measurement units (IMUs) attached to the distal tibia are a validated method of measuring lower-extremity impact accelerations, called tibial accelerations (TAs), in runners. However, no studies have investigated the effects of small errors in IMU placement, which would be expected in real-world, autonomous use of IMUs. The purpose of this study was to evaluate the effect of a small proximal shift in IMU location on mean TAs and relationships between TAs and ground reaction force loading rates. IMUs were strapped to 18 injury-free runners at a specified standard location (∼1 cm proximal to medial malleolus) and 2 cm proximal to the standard location. TAs and ground reaction forces were measured while participants ran at self-selected and 10% slower/faster speeds. Mean TA was lower at the standard versus proximal IMU location in the faster running condition (P =.026), but similar in the slower (P =.643) and self-selected conditions (P =.654). Mean TAs measured at the standard IMU explained more variation in ground reaction force loading rates (r2 =.79−.90; P <.001) compared with those measured at the proximal IMU (r2 =.65−.72; P <.001). These results suggest that careful attention should be given to IMU placement when measuring TAs during running. [ABSTRACT FROM AUTHOR]

Among domestic historians of Pedagogy and education, there has traditionally been a high interest in various aspects of the history of the development of teaching of various disciplines at Moscow University, which is in fact the first classical university in our country. Among the academic subjects that began to be taught at this university almost immediately after its opening was experimental (experimental) Physics.The relevance of the research topic is determined by the fact that Moscow University has been one of the largest centers of domestic higher physics education for more than 250 years, and the state of teaching Physics at this university had a significant impact on the organization of Physics education in our country throughout this period. The beginning of the formation of forms and methods of teaching Physics at Moscow University, as well as the formation of the content of this educational course, dates back to the first decades of the university’s existence, however, this particular period in the history of Physics education at Moscow University is currently the least studied. This makes it difficult to build a holistic picture of the development of Physics teaching in a given higher education institution.Statement of the research problem: to study the process of development of teaching experimental Physics at the Imperial Moscow University in the second half of the 18th century.The goal of the research is to identify the features of the initial stage of the formation of forms and methods of teaching the course of experimental Physics at the Imperial Moscow University in 1755–1791, to study the structure and main elements of the content of this course in the period under review.The research method used is the analysis of literary sources (scientific, reference, memoir-biographical) and educational literature (university Physics textbooks of the second half of the 18th century).The results and key conclusions. The initial stage of development of teaching experimental Physics at the Imperial Moscow University took the first 35 years of its existence. During this period, the Department of Physics was organized, which was replaced by a full-time professor (from among foreigners), as well as the consolidation of the lecture form of teaching and the corresponding verbal and visual teaching methods. The lectures were accompanied by the use of a demonstration experiment. Physics was taught in Latin using modern textbooks at that time, written in foreign languages. By the end of the period under review, as part of the experimental Physics course at Moscow University, students studied exclusively Mechanics and some of its practical applications.

Introduction: Wearable exoskeletons assist individuals with mobility impairments, enhancing their gait and quality of life. This study presents the iP3T model, designed to optimize gait phase prediction through the fusion of multimodal time-series data. Methods: The iP3T model integrates data from stretch sensors, inertial measurement units (IMUs), and surface electromyography (sEMG) to capture comprehensive biomechanical and neuromuscular signals. The model's architecture leverages transformer-based attention mechanisms to prioritize crucial data points. A series of experiments were conducted on a treadmill with five participants to validate the model's performance. Results: The iP3T model consistently outperformed traditional single-modality approaches. In the post-stance phase, themodel achieved an RMSE of 1.073 and an R2 of 0.985. The integration of multimodal data enhanced prediction accuracy and reduced metabolic cost during assisted treadmill walking. Discussion: The study highlights the critical role of each sensor type in providing a holistic understanding of the gait cycle. The attention mechanisms within the iP3T model contribute to its interpretability, allowing for effective optimization of sensor configurations and ultimately improving mobility and quality of life for individuals with gait impairments. [ABSTRACT FROM AUTHOR]

The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject's wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols. [ABSTRACT FROM AUTHOR]

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study's purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMosTM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4− and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMosTM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases. [ABSTRACT FROM AUTHOR]

Activities and falls monitoring systems using wearable technology have a promising future. The publicly available datasets are based on a few inertial features only acquired with an accelerometer, gyroscope, smartphone or smart Watches. The activities and falls performed are also less. In this study, a dataset is created by collecting physiological features along with inertial features which will help in developing and validating systems studying the effect of physiological features on the detection and prediction of falls and activities. The dataset consists of 7 activities and 8 falls for inertial data; 2 activities for ECG data; 6 activities for EMG data and 6 activities for GSR data. Basic body parameters like height, weight, etc. along with beats per minute, SpO2 and blood pressure are also recorded for 12 subjects. The collected data is analyzed statistically using a boxplot, pair plot, correlation heatmap and p value. The activities are classified using SVM, KNN, RF and DT. For GSR, more than 90% accuracy is achieved and for EMG, the accuracy is less than 80%. For IMU data, more than 95% accuracy is achieved. The results encourage combining inertial, physiological and basic body parameters to detect and predict falls and activities. [ABSTRACT FROM AUTHOR]

Background: As one of the leading causes of disability in the world, stroke can determine a reduction of balance performance with a negative impact on daily activity and social life. In this study, we aimed to evaluate the effects of sensor-based balance training with exergaming feedback on balance skills in chronic stroke patients. Methods: 21 individuals (11F, 57.14 ± 13.82 years) with a single event of ischemic stroke were randomly assigned to the sensor-based balance training group (SB-group) or the usual care balance training group (UC-group). Both groups received 10 add-on sessions with exergaming feedback (SB-group) or conventional training (UC-group). Clinical and instrumental evaluation was performed before (t0), after (t1), and after one month (t2) from intervention. Participation level was assessed using the Pittsburgh Rehabilitation Participation Scale at the end of each session. Results: The SB-group showed an improvement in postural stability (p = 0.02) when compared to the UC-group. In the evaluation of motivational level, the score was statistically higher in the SB-group with respect to the UC-group (p < 0.01). Conclusion: Except for the improvement in postural stability, no difference was recorded in clinical score, suggesting a comparable gain in both groups. However, patients undergoing sensor-based training exhibited a higher participation score, ultimately indicating the use of this training to improve the adherence to rehabilitation settings, especially in patients with lower compliance. [ABSTRACT FROM AUTHOR]

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses. [ABSTRACT FROM AUTHOR]

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH > 20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used. [ABSTRACT FROM AUTHOR]

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications. [ABSTRACT FROM AUTHOR]

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The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring. [ABSTRACT FROM AUTHOR]

Emergency Vehicle Preemption (EVP) system plays an important role in reducing the response time of emergency vehicles by around 15–50%. Emergency Vehicle location estimation during Global Positioning System (GPS) outages is a challenging task in EVP. To address this issue, a location estimation scheme has been proposed by applying deep output kernel learning and extended Kalman filter using Inertial Measurement Unit (IMU) dead reckoning. The proposed scheme reduces the error in the estimation of velocity, attitude, and position by dynamically adapting the noise parameters of the extended Kalman filter. This system provides navigation solutions for emergency vehicles. Initially, four test routes were selected and the Position, velocity and attitude (pitch and roll angle) were measured and applied to the proposed scheme. Training and testing were conducted for the measured datasets. The performance of the proposed scheme was measured using five statistical methods during training and testing, and a comparison was made with other existing methods. The simulation results show that the proposed scheme performed well, in the four test routes. Finally, two different case studies were conducted using the proposed scheme and the performance was compared with three other methods. According to the results, the proposed scheme showed improvements in detection accuracy compared to the existing methods during GPS outages of 71.94% and 62.83% for Trajectory-1 and Trajectory-2, respectively. [ABSTRACT FROM AUTHOR]

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning. [ABSTRACT FROM AUTHOR]

LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S 2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results. [ABSTRACT FROM AUTHOR]

Ground reaction force (GRF) data is often collected for the biomechanical analysis of running, due to the performance and injury risk insights that GRF analysis can provide. Traditional methods typically limit GRF collection to controlled lab environments, recent studies have looked to combine the ease of use of wearable sensors with the statistical power of machine learning to estimate continuous GRF data outside of these restrictions. Before such systems can be deployed with confidence outside of the lab they must be shown to be a valid and accurate tool for a wide range of users. The aim of this study was to evaluate how accurately a consumer-priced sensor system could estimate GRFs whilst a heterogeneous group of runners completed a treadmill protocol with three different personalised running speeds and three gradients. Fifty runners (25 female, 25 male) wearing pressure insoles made up of 16 resistive sensors and an inertial measurement unit ran at various speeds and gradients on an instrumented treadmill. A long short term memory (LSTM) neural network was trained to estimate both vertical $(GRF_v)$ (G R F v) and anteroposterior $(GRF_{ap})$ (G R F a p) force traces using leave one subject out validation. The average relative root mean squared error (rRMSE) was 3.2% and 3.1%, respectively. The mean $(GRF_v)$ (G R F v) rRMSE across the evaluated participants ranged from 0.8% to 8.8% and from 1.3% to 17.3% in the $(GRF_{ap})$ (G R F a p) estimation. The findings from this study suggest that current consumer-priced sensors could be used to accurately estimate two-dimensional GRFs for a wide range of runners at a variety of running intensities. The estimated kinetics could be used to provide runners with individualised feedback as well as form the basis of data collection for running injury risk factor studies on a much larger scale than is currently possible with lab based methods. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of 'scalograms', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The motivation behind this research is the lack of an underground mining shaft data set in the literature in the form of open access. For this reason, our data set can be used for many research purposes such as shaft inspection, 3D measurements, simultaneous localization and mapping, artificial intelligence, etc. The data collection method incorporates rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, IMU Xsens MTi-30 and Faro Focus 3D. The ground truth data were acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D terrestrial laser scanner stations of a total 273,784,932 of 3D measurement points. This data set provides an end-user case study of realistic applications in mobile mapping technology. The goal of this research was to fill the gap in the underground mining data set domain. The result is the first open-access data set for an underground mining shaft (shaft depth −300 m). [ABSTRACT FROM AUTHOR]

As the demand for spatial positioning continues to grow, positioning methods based on inertial measurement units (IMUs) are emerging as a promising research topic due to their low cost and robustness against environmental interference. These methods are particularly well suited for global navigation satellite system (GNSS)-denied environments and challenging visual scenarios. While existing algorithms for position estimation using IMUs have demonstrated some effectiveness, there is still significant room for improvement in terms of estimation accuracy. Current approaches primarily treat IMU data as simple time series, neglecting the frequency-domain characteristics of IMU signals. This paper emphasizes the importance of frequency-domain information in IMU signals and proposes a novel neural network, WINNet (Wavelet Inertial Neural Network), which integrates time- and frequency-domain signals using a wavelet transform for spatial positioning with inertial sensors. Additionally, we collected ground-truth data using a LiDAR setup and combined it with the TLIO dataset to form a new IMU spatial positioning dataset. The experimental results demonstrate that our proposed method outperforms the current state-of-the-art inertial neural network algorithms in terms of the ATE, RTE, and drift error metrics overall. [ABSTRACT FROM AUTHOR]

The Galileo High Accuracy Service (HAS) has undergone substantial development in recent years, offering users free access to GPS and Galileo satellite orbit, clock, and code bias corrections for Precise Point Positioning (PPP) on a global scale. This paper explores the use of the currently available HAS corrections for smartphone positioning. Due to hardware disparities and limited tracking capabilities, smartphone processing with only two GNSS constellations struggles to ensure satisfactory satellite geometry and sufficient observations in realistic user environments. To fully harness all observed measurements and the orbit and clock information directly disseminated from satellites, this study introduces a new PPP algorithm combing HAS corrections and broadcast ephemerides (HAS and BRDC PPP) for smartphone processing. Through four vehicle experiments in urban environments, the proposed HASandBRDC PPP solutions demonstrated a notable reduction in positioning errors. Specifically, the horizontal rms and 95th percentile error decreased from 2.0 and 3.3 m to 1.6 and 2.4 m, respectively, when compared to the HAS PPP solutions. These results are highly comparable to four-constellation PPP solutions utilizing Centre National d'Etudes Spatiales (CNES) ultra-rapid products, which can achieve a horizontal rms of 1.4 m. Additionally, the inclusion of smartphone inertial measurement unit (IMU) measurements results in a notable 59% average reduction in PPP gross errors. This study provides an original comparison of the 2022 and 2023 HAS corrections, demonstrating the feasibility of real-time lane-level navigation with smart devices even in remote areas without Internet connectivity, which has not been previously explored. [ABSTRACT FROM AUTHOR]

Accurately estimating a vehicle's position and velocity in real-time is crucial for navigation, especially in environments where updates must be continuous and reliable. Achieving high-precision localization often requires integrating multiple positioning sources, particularly in indoor settings where satellite-based navigation is impractical. Visual-Inertial Odometry (VIO) systems are commonly employed for this purpose. With the rapid advancements in artificial intelligence and deep learning, particularly the deployment of deep networks on GPU-equipped platforms, VIO systems have seen widespread adoption in recent years. However, these systems are vulnerable to remote attacks, such as laser-induced disruptions that can impair camera lenses, leading to compromised image capture and degraded visual localization. This paper introduces a highly-robust system utilizing a shallow convolutional neural network and a fully connected detection layer to enhance VIO resilience against such threats. Moreover, for power-sensitive applications, such as those relying on batteries, an Optimized Kalman Filter (OKF) is used to merge two distinct positioning sources, offering a more efficient alternative to recurrent neural networks like LSTMs. The proposed system demonstrates a 13.27% improvement in accuracy over existing robust VIO systems designed to counteract noise and distortion.

The REMOT project originates from a very specific end-user need, and in this sense is a strong example of open innovation. The idea is to apply a technology that is already used in other fields, based on the integration of inertial sensors (IMU) and satellite receivers (GNSS), to track the kinematics of the human body with great precision, reliability and continuity.

IntroductionThe shift of the horse breeding sector from agricultural to leisure and sports purposes led to a decrease in local breeds’ population size due to the loss of their original breeding purposes. Most of the Italian breeds must adapt to modern market demands, and gait traits are suitable phenotypes to help this process. Inertial measurement unit (IMU) technology can be used to objectively assess them. This work aims to investigate on IMU recorded data (i) the influence of environmental factors and biometric measurements, (ii) their repeatability, (iii) the correlation with judge evaluations, and (iv) their predictive value.Material and methodsThe Equisense Motion S® was used to collect phenotypes on 135 horses, Bardigiano (101) and Murgese (34) and the data analysis was conducted using R (v.4.1.2). Analysis of variance (ANOVA) was employed to assess the effects of biometric measurements and environmental and animal factors on the traits.Results and discussionVariations in several traits depending on the breed were identified, highlighting different abilities among Bardigiano and Murgese horses. Repeatability of horse performance was assessed on a subset of horses, with regularity and elevation at walk being the traits with the highest repeatability (0.63 and 0.72). The positive correlation between judge evaluations and sensor data indicates judges’ ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). A high variability was observed in the accuracy of the SVM model, ranging from 55 to 100% while the other two models showed higher consistency, with accuracy ranging from 74 to 100% for the GBM and from 64 to 88% for the KNN. Overall, the GBM model exhibits the highest accuracy and the lowest error. In conclusion, integrating IMU technology into horse performance evaluation offers valuable insights, with implications for breeding and training.

In response to the problems of cumulative errors in step size estimation and the large sample size required by traditional deep learning methods in the pedestrian dead reckoning (PDR) based underground personnel positioning system in coal mines, a step size estimation method for underground personnel based on generative adversarial network (GAN) is proposed. The GAN model mainly includes two parts: generative model and discriminative model, both of which are implemented using deep neural networks (DNNs). The generative model aims to generate continuous result distributions (i.e. labels) based on input data. Its output layer uses a linear activation function to preserve the linear features of the network, allowing the model to predict the step size of any personnel during walking. The discriminant model aims to distinguish whether the input data and labels are real labels or labels generated by the generator. Its output layer uses a Sigmoid activation function to achieve binary classification of results. After determining the generative model and discriminant model, the GAN model combines two models for training. By constructing and optimizing the dynamic competition between the generator and discriminator, the generator can learn to generate more realistic and indistinguishable data samples in continuous iterations. The experimental results show that under the same training and testing sets, the average error of the GAN model is 0.14 m, and the standard deviation and root mean square error are both smaller than those of the DNNs model, with the minimum values being 0.74 m. The outdoor test results show that the GAN based underground personnel step estimation method has a minimum error of 3.21% and a maximum error of 4.79% in uphill and downhill scenarios. Compared to uphill and downhill scenarios, the error in playground scenarios is smaller, with a maximum error of 1.91%.