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This proposal encompasses a comprehensive investigation into non-invasive cardiovascular monitoring techniques, which involved developing and integrating novel signal processing algorithms and machine learning (ML) architectures. We introduced a unique algorithm employing a novel intra-beat biomarker, designed for correcting raw BP data from various sensor sources. Our diastolic transit time (DTT)-based approach successfully eliminated stochastic baseline wander, enabling accurate, single-sensor beat-to-beat blood pressure (BP) measurements. This method not only maintained the spatiotemporal integrity of signals but also mitigated motion artifacts, paving the way for its application in continuous non-invasive BP technologies for acute, outpatient, and home settings. Our investigations into beat-to-beat heart rate and BP variability revealed significant physiological insights, particularly in stroke and sickle cell disease (SCD) patients. We observed increased systolic BP variability in stroke patients compared to healthy controls, a finding that underscores the potential of BP variability as a marker for long-term monitoring and management. In SCD patients, our study highlighted the impact of mental and psychological stressors on BP variability and vascular tone, revealing a complex interplay between autonomic nervous system dysregulation, endothelial dysfunction, and vascular tone alterations. These insights suggest the need for holistic management strategies that address both physiological and psychosocial aspects of disease. The application of ML in predictive health analytics marks a significant advancement in non-invasive monitoring of cardiac output (CO) and intracranial pressure (ICP). We developed a model for real-time, accurate CO monitoring using arterial line BP data, demonstrating the potential of ML to revolutionize acute care patient management. Additionally, our work in developing a non-invasive sensing system for continuous ICP measurement through be

Automated Human Activity Recognition (HAR) stems from the requirement to seamlessly integrate technology into daily life, to enhance user experience, improve healthcare, provide improved operations, ensure safety, deliver data-driven insights, and address various real-world challenges. However, unscripted Human activity faces challenges that must be understood, and require advances in sensor technology and machine learning models. This paper presents an Active Transfer Learning (ATL) approach for real-time HAR using mobile sensor data. Unlike traditional methods, our approach accounts for both the physical and habitual constraints of individuals. Existing works make an unrealistic assumption of an omniscient oracle while using the same datasets for both training and testing of the models, which makes them impractical for industry requirements. Our proposed approach addresses challenges in existing HAR algorithms, proposing a methodology to adapt models to the real-world datasets while training and testing on cross datasets. We have tailored an existing Entropy and Memory Maximization algorithm to work in a real-time environment while considering user constraints. Primarily trained in a well-labeled controlled environment dataset, we introduce noise injection to prevent the model from overfitting and enhance its generalization for scarcely labeled real-world datasets. Evaluations on publicly available datasets demonstrate our approach achieves 80% - 90% accuracy, outperforming the base algorithm accuracy of 12% - 14%. Importantly, our proposed technique outperforms with limited labeled data, making it valuable for real-time scenarios where labeling is sparse. This research advances HAR in real-world settings, offering improved accuracy and adaptability., QC 20240722

Le vieillissement de la population, aggravé par la pénurie de personnel médical, est devenu un problème important dans la plupart des pays développés. Il devient évident que la nécessité de maintenir les aînés à la maison ne peut être évitée. Pour assurer leur bien-être et leur sécurité, le concept de maison intelligente apparaît comme la solution, en surveillant leurs activités quotidiennes et en les accompagnant en cas de besoin. Dans la littérature scientifique, la plupart des travaux se concentrent sur la surveillance d’activités de haut niveau comme manger, marcher ou dormir. Cependant, pour identifier toute exécution erronée, nous avons besoin d’un système capable de reconnaître les étapes spécifiques d’une tâche en temps réel et d’avoir une reconnaissance plus détaillée. Dans ce mémoire, nous proposons une approche algorithmique pour la reconnaissance d’activités à granularité fine basée sur la photopléthysmographie. Notre modèle est basé sur un jeu de données collectées à partir d’un bracelet équipé d’accéléromètres et de capteurs photopléthysmographiques, appliqué à des techniques d’apprentissage automatique. Pour construire l’ensemble de données, nous avons défini un ensemble de gestes de cuisson atomiques qui ont été effectués par 20 participants. Les données collectées ont été annotées et seront mises à la disposition de la communauté scientifique. Nous avons obtenu des résultats prometteurs, avec une exactitude de 94 %, démontrant le potentiel des capteurs photopléthysmographiques dans les maisons intelligentes pour la reconnaissance des activités dans le cadre d’une assistance aux personnes âgées en perte d’autonomie, un domaine où ils ont été rarement utilisés.

In the context of the H2020-funded project SINTEC, a comparative study between Bluetooth Low Energy (BLE) and fat-intrabody communication (Fat-IBC) has been done with two advanced torso phantom models and two human volunteers, representing low-fat and high-fat content. The phantom tests were done in a small semi-shielded chamber made for the tests with the possibility of suppressing the surface wave in the air-skin surface. Two Fat-IBC antennas were designed: a type of loop antenna, and a type of folded dipole antenna, both placed parallel to the skin. The Fat-IBC antennas perform better than the BLE one both on phantom and volunteers so far and more in the front-to-back (About 10 - 20 dB times better). For the athletic phantom case, the Fat-IBC antennas showed a reduction of about 5 dB in the transmission coefficient. Fat-IBC propagation mechanism and surface wave suppression need to be further studied, but the current results are positive on the significance of the Fat-IBC mechanism to reduce surface leakage and associated security/safety concerns in body-centric communication scenarios.

Nurses experience work-related musculoskeletal disorders (WMSDs) such as lower back pain due to awkward postures or movements during patient handling. Monitoring and education for patient handling are necessary to prevent these WMSDs. Recently, measurement methods for patient handling using wearable sensors have been developed to implement these interventions at various sites. However, the status of these measurement methods has not been comprehensively summarized. The purpose of this study is to summarize the status of measurement methods for patient handling using wearable sensors. Peer-reviewed papers published between January 2013 and November 2023 that included measurements of patient handling using wearable sensors were selected from Google Scholar. Measured patient handlings, postures, and movements were summarized. The type, number, and placement of sensors were also investigated. Furthermore, the applied data processing techniques were also summarized. Inertial sensors and insole pressure sensors were applied for measurement methods. Current methods can measure trunk angle, arm movement, and foot placement during several motions such as patient transfer. In addition, load and correctness of patient handling motion are recognized by a wearable sensor-based system using machine learning techniques. These results indicate that current methods can provide effective kinematic values during patient handling to prevent WMSDs. On the other hand, there were also limitations due to number of sensors. Future studies should develop simpler measurement methods using fewer sensors.

Development in the field of smart wearable products for monitoring daily life health status is beginning to spread in society. Textile electronic methods are improving and facilitating the manufacturing of sensorized garments. This paper evaluates a newly developed t-shirt incorporating electronic sensing and interconnecting elements integrated into the T-shirt with textile-friendly techniques sensorized with a Movesense device for monitoring ECG and HR and activity. The measurement results obtained from the t-shirt are entirely in agreement with the measurements obtained with other textile garments and encourage us for a near future where wearable sensors are just textile garments sensorized seamlessly without suboptimal textile-electronic integrated elements.

Background: The fraction of elderly people in the population is growing, the incidence of some cancers is increasing, and the number of available cancer treatments is evolving, causing a challenge to healthcare systems. New healthcare tools are needed, and wearable sensors could partly be potential solutions. The aim of this case report is to describe the Danish research experience with wearable sensors in oncology reporting from three oncological wearable research projects. Case studies: Three planned case studies investigating the feasibility of different wearable sensor solutions during cancer treatment are presented, focusing on study design, population, device, aim, and planned outcomes. Further, two actual case studies performed are reported, focusing on patients included, data collected, results achieved, further activities planned, and strengths and limitations. Results: Only two of the three planned studies were performed. In general, patients found the technical issues of wearable sensors too challenging to deal with during cancer treatment. However, at the same time it was demonstrated that a large amount of data could be collected if the framework worked efficiently. Conclusion: Wearable sensors have the potential to help solve challenges in clinical oncology, but for successful research projects and implementation, a setup with minimal effort on the part of patients is requested. Keywords: wearable sensors; oncology; feasibility; cancer; eHealth, Background: The fraction of elderly people in the population is growing, the incidence of some cancers is increasing, and the number of available cancer treatments is evolving, causing a challenge to healthcare systems. New healthcare tools are needed, and wearable sensors could partly be potential solutions. The aim of this case report is to describe the Danish research experience with wearable sensors in oncology reporting from three oncological wearable research projects. Case studies: Three planned case studies investigating the feasibility of different wearable sensor solutions during cancer treatment are presented, focusing on study design, population, device, aim, and planned outcomes. Further, two actual case studies performed are reported, focusing on patients included, data collected, results achieved, further activities planned, and strengths and limitations. Results: Only two of the three planned studies were performed. In general, patients found the technical issues of wearable sensors too challenging to deal with during cancer treatment. However, at the same time it was demonstrated that a large amount of data could be collected if the framework worked efficiently. Conclusion: Wearable sensors have the potential to help solve challenges in clinical oncology, but for successful research projects and implementation, a setup with minimal effort on the part of patients is requested.

The paper presents the 7R algorithm of the circular economy principle in realizing wearable sensors. The application of additive manufacturing in the realization of sensors is essential from the point of view of sustainable production, which starts from the material and ends with its recycling process. All seven principles and their connection with additive manufacturing as a critical element in the circular economy are presented. The paper defines the theoretical framework for realizing a sustainable wearable sensor. The production of such sensors primarily refers to the application of flexible 3D printing and electronic components that can be quickly replaced, modified, disassembled, and recycled.

More than 80% of people will experience back pain in their lifetime. Recent data indicates that lower back pain is the leading cause of disability globally, and the problem is getting worse, not better. Lower back injuries leading to disabling pain are especially common in stooped work occupations, which are characterised by workers spending long periods of time with a fully flexed torso and mostly straight legs. Dysfunction in neuromuscular control could predict injury, however as these kinds of injuries take a long time to develop, they are almost never observed alongside this data. Advances in wearable sensors allow for the collection of this data outside the lab, but due to the lack of labelled injury data, there's no easy way to connect these measurements to injury. Additionally, neuromuscular control is task-specific, which means any dysfunction is also task-specific. So different measurements may be required depending on the task, and when it comes to real occupations it's not clear which measurements are the most useful indicators of injury risk. In this work, I propose a novel unsupervised feature selection framework to identify key indicators of lower back injury in a task specific way. It is noted that the vast majority of lower back injuries occur over time via repetitive stress. The lower back injury risk continually 'gets worse', and workers doing the same tasks are exposed to similar stresses. The proposed framework analyses which features contribute to this underlying shared monotonic trend of injury development. The framework is evaluated with data collected from Australian sheep shearers, who experience extreme rates of lower back injury. Features identified by the framework are consistent with the literature on lower back pain, and outperform commonly used unsupervised feature selection techniques when evaluated using a pseudo-ground-truth. Utilising the identified features, a prototype wearable sensor was developed to obtain these measurements fr

Ergonomic assessments to determine risks of work-related musculoskeletal disorders as well as to compare designs of work-tasks and workstations, are imperative for high sustainability and productivity in any given industry. Hence, assessment tools that can objectively and reliably capture postures, joint angles and muscle activities play very important role in properly determining risks relating to work and various tasks. The introduction of direct measurement instruments/tools has been helping and continue to help improve upon observational assessment methods to attain objectivity and reliability. This project aimed at contributing to future improvements of industrial risk assessment measures in ergonomics by identifying and testing direct measurement instruments/tools that can enhance observational risk assessment methods and introduce a new way of signal processing, hence, reducing assessment time while increasing objectivity and reliability. Several candidate instruments were identified and out of the identified, ten were selected as potential candidates. Two out of the ten, Wergonic and ErgoHandMeter were then selected and tested on common observation risk assessment factors that could be measured and answers provided directly or by analyses. The Wergonic instrument was modified to enhance its measuring capability from one fully and partially two factors to six factors. New algorithms were also employed to analyse measurements of the ErgoHandMeter in order to answer questions regarding repetitive movements. The two instruments tested, are able to measure and provide results for six commonly and one rarely assessed biomechanical risk factors. By combining selected potential candidates, many of the commonly targeted biomechanical risk factors in observational instruments can be measured by the selected direct measurement instruments. However, some factors especially force measurement remain a challenge for measuring by direct wearable sensor instruments.

This paper describes a localization method with wearable electromagnetic sensor and orientation sensor for wearable computer users. Many user localization methods have been investigated to realize location-based services in a wide environment. The localization methods usually employ a hybrid approach in which user's position is estimated by using positioning infrastructures and dead reckoning such as a pedometer. However, the installation cost of infrastructures increases when the area expands, and the error of the pedometer is frequently caused by failures in walking locomotion detection and the difference between ideal and estimated step lengths. If the relative distance is accurately estimated by dead reckoning approach, the installation cost of infrastructures can be reduced. This paper proposes a new localization method that improves the estimation accuracy of step length in dead reckoning approach. The proposed method measures user's orientation and geometrical relationship between user's heel and waist with an orientation sensor and an electromagnetic tracker attached to user's body. When both feet come into contact with the ground, user's position is updated by adding the estimated step length which means the relationship between user's heels and the position estimated in the previous step. The proposed localization method has been experimented using a prototype system to evaluate the accuracy of the proposed method., ISWC'06 : IEEE International Symposium on Wearable Computers , Oct 11-14, 2006 , Montreux, Switzerland

This paper describes a wearable augmented reality system using positioning infrastructures and a pedometer. To realize augmented reality systems, the position and orientation of user's viewpoint should be obtained in real time. The proposed system measures the orientation of user's viewpoint by an inertial sensor and the user's position using positioning infrastructures in environments and a pedometer. The system specifies the user's position using the position ID received from RFID tags or IrDA markers which are the components of positioning infrastructures. When the user goes away from them, the user's position is alternatively estimated by using a pedometer. We have developed a navigation system using the proposed techniques and have proven the feasibility of the system with experiments., ISMAR 2003 : IEEE / ACM International Symposium on Mixed and Augmented Reality , Oct 7-10, 2003 , Tokyo, Japan

This paper describes a wearable augmented reality systemusing positioning infrastructures and a pedometer. Torealize augmented reality systems, the position and orientationof user's viewpoint should be obtained in real time. Theproposed system measures the orientation of user's viewpointby an inertial sensor and the user's position using positioninginfrastructures in environments and a pedometer.The system specifies the user's position using the positionID received from RFID tags or IrDA markers which are thecomponents of positioning infrastructures. When the usergoes away from them, the user's position is alternatively estimatedby using a pedometer. We have developed a navigationsystem using the proposed techniques and have proventhe feasibility of the system with experiments., ISWC 2003 : IEEE International Symposium on Wearable Computers , Oct 20-23, 2003 , Sanibel Island, Florida, USA

IoT is currently transforming the technological infrastructure. The Internet of Things has made it possible for us to integrate a variety of complex systems quickly and easily, including smart home appliances, smart traffic control systems, smart office systems, smart environments, smart vehicles, and smart temperature control systems, etc. One of the most notable IoT applications is health monitoring. This project would support real-time activity and monitor the healthcare system for elderly citizens. The information collected by different sensors in real time is stored in a local server which thereby connects the caretaker, and ambulance at the time of emergency for the right information. This research provides an IoT-based system for elderly people who lives alone, that can track and record the health parameters using different wearable sensors and detect fall using fall detection system and include mobile user interface and protocols for medication alarms.

Many sweat-based wearable monitoring systems have been recently proposed, but the data provided by those systems often lack a reliable and meaningful relation to standardized blood values. One clear example is lactate, a relevant biomarker for both sports and health sectors, with a complex sweat–blood bioequivalence. This limitation decreases its individual significance as a sweat-based biomarker. Taking into account the insights of previous studies, a multiparametric methodology has been proposed to predict blood lactate from non-invasive independent sensors: sweat lactate, sweat rate, and heart rate. The bioequivalence study was performed with a large set of volunteers (>30 subjects) in collaboration with sports institutions (Institut Nacional d’Educació Física de Catalunya, INEFC, and Centre d’Alt Rendiment, CAR, located in Spain). A neural network algorithm was used to predict blood lactate values from the sensor data and subject metadata. The developed methodology reliably and accurately predicted blood lactate absolute values, only adding 0.3 mM of accumulated error when compared to portable blood lactate meters, the current gold standard for sports clinicians. The approach proposed in this work, along with an integrated platform for sweat monitoring, will have a strong impact on the sports and health fields as an autonomous, real-time, and continuous monitoring tool, The authors kindly acknowledge the support from the Spanish Ministerio de Indústria, Energía y Turismo (AEI Clusters Program, AEI2009L1CA011), Ministerio de Ciencia y Innovació n (PID2020-114070RB-I00), Agencia Estatal de Investigación (RED2018-102829-T and CPP2021-009021), and AGAUR (2019 DI 18 and 2021PROD00064), Postprint (published version)

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost ( 150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (p < 0.05) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.

Stroke is a leading cause of disability, with over 80% of patients experiencing chronic upper extremity (UE) impairments that impede daily activities and degrade quality of life. While wearable sensors in the form of step counters have foundapplication in stroke rehabilitation, effective home rehabilitation of the UE using wearable sensors remains an open question. This dissertation focuses on three research questions in UE rehabilitation using a wrist-worn wearable inertial measurement unit (IMU): (1) identifying hand movements, (2) assessing the quality of movement experience (QOME) in daily life, and (3) identifying ways to improve UE QOME with wearable feedback.We begin by reviewing existing wearable sensor technologies for at-home rehabilitation, setting the stage for our exploration of hand movement identification and arm movement quality assessment. We then introduce a spectrogram-basedconvolutional neural network (CNN) algorithm for hand movement recognition using a single wrist-worn inertial measurement unit (IMU). Our working hypothesis was that we could use machine learning to identify active flexion and extension of the fingers/wrist based on the vibrational patterns produced at the distal end of the forearm. Using wristworn IMU recordings from 22 individuals with a stroke, we found we could identify the occurrence of finger/wrist movements with approximately 75% accuracy. Thus, ringless sensing of finger/wrist movement occurrence is feasible using wrist-worn IMUs, opening up new avenues for hand-related healthcare applications.Subsequently, we posited that it may be more effective to encourage an increase in beneficial patterns of movement (i.e. QOME), rather than simply the overall amount of movement. As a first step toward this goal, we sought to identify statisticalcharacteristics of daily arm movements that become more prominent as arm impairment decreases. Using the same data set as for the hand movement study, we identified several measures that i

Pregnancy is a challenging time for maintaining quality sleep and managing stress. Digital self-monitoring technologies are popular because of assumed increased patient engagement leading to an impact on health outcomes. However, the actual association between wear time of such devices and improved sleep/stress outcomes remains untested. Here, a descriptive comparative pilot study of 20 pregnant women was conducted to examine associations between wear time (behavioral engagement) of self-monitoring devices and sleep/stress pregnancy outcomes. Women used a ring fitted to their finger to monitor sleep/stress data, with access to a self-monitoring program for an average of 9½ weeks. Based on wear time, participants were split into two engagement groups. Using a linear mixed-effects model, the high engagement group showed higher levels of stress and a negative trend in sleep duration and quality. The low engagement group showed positive changes in sleep duration, and quality and experienced below-normal sleep onset latency at the start of the pilot but trended toward normal levels. Engagement according to device wear time was not associated with improved outcomes. Further research should aim to understand how engagement with self-monitoring technologies impacts sleep/stress outcomes in pregnancy.

Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (+/- SD) absolute cloth-skin sensor differences of the median dominant arm elevation angle ranged between 1.2 degrees (+/- 1.4) and 4.1 degrees (+/- 3.5). For the median trunk flexion angle, the mean absolute cloth-skin sensor differences ranged between 2.7 degrees (+/- 1.7) and 3.7 degrees (+/- 3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners., QC 20230529

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors., This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00., Peer Reviewed, Postprint (published version)

The combination of sensors and microfluidics has become a promising approach for detecting a wide variety of targets relevant in biotechnology. Thanks to recent advances in the manufacturing of microfluidic systems, microfluidics can be manufactured faster, cheaper, and more accurately than ever before. These advances make microfluidic systems very appealing as a basis for constructing sensor systems, and microfluidic devices have been adapted to house (bio)sensors for various applications (e.g. protein biomarker detection, cell culture oxygen control, and pathogen detection). This review article highlights several successfully integrated microfluidic sensor systems, with a focus on work that has been published within the last two years. Different sensor integration methods are discussed, and the latest trends in wearable- and smartphone-based sensors are described.

Migraine is one of the most challenging diseases, putting a strain on health services and lowering the quality of life of those who suffer from it. By identifying the pre-symptoms of migraine and using self-measurement technology, detailed information can be collected, and health problems can be addressed. This study explored migraine patients’ perceptions of the benefits they perceive to be important when using technology applications in the self-care of their migraine condition. The aim of this study in the field of computer science was to investigate the technological solutions, the use, and the patients’ expectations of the benefits of wearable sensors. Previous studies have not combined these three perspectives. This study analysed the willingness of migraine patients to use wearable sensors to support self-care and the use of an electronic migraine diary for migraine management. The study was carried out using a qualitative multi-method study. The research data was based on 582 email survey responses, 12 qualitative interviews and user experiences of the Empatica E4 device. The study showed that migraine patients wanted wearable sensors to support self-care by identifying the pre-symptoms of migraine and an electronic migraine diary to monitor migraine attacks. Together, the wearable device and the electronic migraine diary support the patient’s self-care. The dissertation resulted in a perspective model of wearable sensors. The model provides new insights from three different perspectives: technologies and biosignals, user experiences and benefit expectations to support self-care of migraine patients., Tiivistelmä Migreeni on yksi haastavimmista sairauksista, joka kuormittaa terveydenhuollon palveluja ja laskee sitä sairastavien elämänlaatua. Migreenin ennakko-oireiden tunnistamisella ja itsensä mittaamisen teknologialla mahdollistetaan yksityiskohtaisen tiedon kerääminen ja terveysongelmiin vastaaminen. Tässä tutkimuksessa selvitettiin migreenipotilaiden käsityksiä siitä, mitä hyötyjä he itse pitävät tärkeänä teknisten sovellusten käytössä migreenisairautensa omahoidossa. Tämän tietojenkäsittelytieteenalaan kuuluvan tutkimuksen tavoitteena oli tutkia puettavien sensoreiden teknologiaratkaisuja, käyttöä ja potilaiden hyötyodotuksia. Aikaisemmat tutkimukset eivät ole yhdistäneet näitä kolmea näkökulmaa. Tässä tutkimuksessa analysoitiin migreenipotilaiden halukkuutta käyttää puettavia sensoreita omahoidon tukena ja sähköisen migreenipäiväkirjan hyödyntämistä migreenin hoidossa. Tutkimus toteutettiin laadullisella monimenetelmätutkimuksella. Tutkimusaineisto perustui 582 sähköpostikyselyn vastauksiin, 12 laadulliseen haastatteluun ja 11 Empatica E4 -laitteen käyttäjäkokemuksiin. Tutkimus osoitti, että migreenipotilaat toivoivat omahoidon tueksi puettavia sensoreita tunnistamaan migreenin ennakko-oireita sekä sähköisen migreenipäiväkirjan migreenikohtauksien seurantaan. Puettava laite ja sähköinen migreenipäiväkirja yhdessä tukevat potilaan omahoitoa. Väitöskirjan tuloksena syntyi puettavien sensoreiden näkökulmamalli, joka tuo uutta tietoa kolmesta eri näkökulmasta, jotka ovat teknologiat ja biosignaalit, käyttäjäkokemukset sekä hyötyodotukset migreenipotilaiden omahoidon tueksi.

There has been a long-standing interest in controlling and instrumenting the human body. Whether to restore lost function with neural prosthetics, monitor blood glucose levels, or augment human capabilities, there are countless opportunities for sensors inside ( e.g., ingestible, injectable, and implantable) and outside ( e.g., wearable) the body. However, many challenges exist when instrumenting the body. First, many use cases ( e.g., implanted sensors) require long-term recording to capture anomalous behavior—sometimes with limited accessibility—necessitating ultralow power consumption. Second, the power reduction challenge is further exacerbated by size constraints, which limit battery capacity or harvestable energy levels. Third, the signals of interest are often low bandwidth (kHz) and small in amplitude (µV to mV); thus, low-noise front ends are needed. Addressing these challenges has led to a large body of work on the design of highly power-efficient, low-noise amplifiers for biomedical integrated circuits., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Microelectronics

Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user’s performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS). Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957). Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70–0.80; MAE = 0.48–0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00–0.19; MAE = 1.15–1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter. Discussion: Our preliminary results indi, Peer Reviewed, Postprint (published version)

Environmental stressors in city traffic are a relevant health threat to urban cyclists and pedestrians. These stressors are multifaceted and include noise pollution, heat, and air pollution such as particulate matter. In the present chapter, we describe the use of wearable sensors carried by volunteers to capture their exposure to environmental stressors on their everyday routes. These wearable sensors are becoming increasingly important to capture the spatial and temporal distribution of environmental factors in the city. They also offer the unique opportunity to provide individualized feedback to the person wearing the sensor as well as possibilities to visualize different stressors in their temporal and spatial distribution in a virtual reality environment. We used the option of providing individualized feedback on personal exposure levels in two randomized controlled field studies. In these experiments, we studied the psychological health-related outcomes of carrying a wearable sensor and receiving feedback on one’s individual exposure levels.

[Abstract]: People whose jobs involve repetitive motions of the wrist and forearm can suffer from lateral epicondylitis, which is a significant burden on both the individual and the employer due to treatment costs, reduced productivity, and work absenteeism. This paper describes an ergonomic intervention to reduce lateral epicondylitis in the workstation of a textile logistics center. The intervention includes workplace-based exercise programs, evaluation of risk factors, and movement correction. An injuryand subject-specific score was calculated from the motion captured with wearable inertial sensors at the workplace to evaluate the risk factors of 93 workers. Then, a new working movement was adapted to the workplace, which limited the observed risk factors and took into account the subject-specific physical abilities. The movement was taught to the workers during personalized sessions. The risk factors of 27 workers were evaluated again after the intervention to validate the effectiveness of the movement correction. In addition, active warm-up and stretching programs were introduced as part of the workday to promote muscle endurance and improve resistance to repetitive stress. The present strategy offered good results at low cost, without any physical modification of the workplace and without any detriment to productivity.

With the advancements in wearable technology, continuous monitoring of physiological and biomechanical parameters has become possible, providing valuable insights into health and fitness. This technology allows for remote patient monitoring, medical support, and even virtual coaching. One area of focus is gait analysis, which can help reduce the risk of running-related injuries. This study aims to develop a small, lightweight, and affordable wireless sensor device for collecting gait data. The device utilizes IMU sensors positioned on the pelvis and foot to measure stability and detect changes due to fatigue during running. Precise time synchronization between the sensors and an Android phone is crucial for accurate data analysis. The study employs Bluetooth LE technology for wireless transmission and proposes a synchronization method to align the data from the asynchronous sensors. The hardware and software components of the sensor device are described, along with the synchronization process. Additionally, a pilot study involving the use of the sensors during treadmill running was performed.

Vaccines are among the most powerful tools to combat the COVID-19 pandemic. They are highly effective against infection and substantially reduce the risk of severe disease, hospitalization, ICU admission, and death. However, their potential for attenuating long-term changes in personal health and health-related wellbeing after a SARS-CoV-2 infection remains a subject of debate. Such effects can be effectively monitored at the individual level by analyzing physiological data collected by consumer-grade wearable sensors. Here, we investigate changes in resting heart rate, daily physical activity, and sleep duration around a SARS-CoV-2 infection stratified by vaccination status. Data were collected over a period of 2 years in the context of the German Corona Data Donation Project with around 190,000 monthly active participants. Compared to their unvaccinated counterparts, we find that vaccinated individuals, on average, experience smaller changes in their vital data that also return to normal levels more quickly. Likewise, extreme changes in vitals during the acute phase of the disease occur less frequently in vaccinated individuals. Our results solidify evidence that vaccines can mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms of duration and magnitude. Furthermore, they demonstrate the value of large-scale, high-resolution wearable sensor data in public health research.

Health monitoring systems that incorporate stretchable sensors can provide real-time feedback on a person's physical activity, stress levels, and overall well-being. For instance, stretchable sensors based on conductive hydrogels have emerged as a promising technology for monitoring physiological signals such as heart rate, respiration, and muscle activity. For developing such systems, a strain machine is accounted as the first requirement for laboratories focusing on developing conductive hydrogels as strain sensors. In this article, we present a programmable, user-friendly, and versatile 3D-printed setup designed and fabricated for measuring the strain and strain sensitivity of soft electronic materials. The setup comprises a computer (or mobile), an impedance analyzer, and the strain machine that are required for developing stretchable strain sensors in scientific laboratories. We provide a very detailed build manual on how to manufacture and use the strain machine, using an ionic conductive hydrogel based on polyacrylic acid and polyvinyl alcohol as a model system. The results demonstrated the excellent performance of the strain machine in applying programmable strain regimes to the material of interest. By offering clear instructions and a build manual, our work enables scientific researchers to construct an affordable and user-friendly strain machine to boost their research progress.

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.

Several studies have analyzed heart rate variability (HRV) using nonlinear methods, such as approximate entropy, the largest Lyapunov exponent, and correlation dimension in patients with cardiovascular disorders. However, few studies have used nonlinear methods to analyze HRV in order to determine the level of physical fatigue experienced by construction workers. As a result, to identify and categorize physical fatigue in construction workers, the current study examined the linear and nonlinear approaches of HRV analysis. Fifteen healthy construction workers (mean age, 33.2±6.9 years ) were selected for this study. A textile-based wearable sensor monitored each participant’s HRV after they completed 60 min of bar bending and fixing tasks. At baseline, 15, 30, 45, and 60 min into the task, participants were given the Borg-20 to measure their subjective levels of physical fatigue. Nonlinear [e.g., R-R interval (RRI) variability, entropy, detrended fluctuation analysis] and linear (e.g., time- and frequency-domain) HRV parameters were extracted. Five machine learning classifiers were used to identify and discern different physical fatigue levels. The accuracy and validity of the classifier models were evaluated using 10-fold cross-validation. The classification models were developed by either combining or individualized HRV features derived from linear and nonlinear HRV analyses. In the individualized feature sets, time-domain features had the highest classification accuracy (92%) based on the random forest (RF) classifier. The combined feature (i.e., the time-domain and nonlinear features) sets showed the highest classification accuracy (93.5%) using the RF classifier. In conclusion, this study showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers. This research offers important contributions to the industry by analyzing the variations in linear and nonlinear HRV parameters in response to constr

Daily wheelchair ambulation is seen as a risk factor for shoulder problems, which are prevalent in manual wheelchair users. To examine the long-term effect of shoulder load from daily wheelchair ambulation on shoulder problems, quantification is required in real-life settings. In this study, we describe and validate a comprehensive and unobtrusive methodology to derive clinically relevant wheelchair mobility metrics (WCMMs) from inertial measurement systems (IMUs) placed on the wheelchair frame and wheel in real-life settings. The set of WCMMs includes distance covered by the wheelchair, linear velocity of the wheelchair, number and duration of pushes, number and magnitude of turns and inclination of the wheelchair when on a slope. Data are collected from ten able-bodied participants, trained in wheelchair-related activities, who followed a 40 min course over the campus. The IMU-derived WCMMs are validated against accepted reference methods such as Smartwheel and video analysis. Intraclass correlation (ICC) is applied to test the reliability of the IMU method. IMU-derived push duration appeared to be less comparable with Smartwheel estimates, as it measures the effect of all energy applied to the wheelchair (including thorax and upper extremity movements), whereas the Smartwheel only measures forces and torques applied by the hand at the rim. All other WCMMs can be reliably estimated from real-life IMU data, with small errors and high ICCs, which opens the way to further examine real-life behavior in wheelchair ambulation with respect to shoulder loading. Moreover, WCMMs can be applied to other applications, including health tracking for individual interest or in therapy settings., Biomechatronics & Human-Machine Control, Biomechanical Engineering

Assessing post-operative recovery is a significant component of perioperative care, since this assessment might facilitate detecting complications and determining an appropriate discharge date. However, recovery is difficult to assess and challenging to predict, as no universally accepted definition exists. Current solutions often contain a high level of subjectivity, measure recovery only at one moment in time, and only investigate recovery until the discharge moment. For these reasons, this research aims to create a model that predicts continuous recovery scores in perioperative care in the hospital and at home for objective decision making. This regression model utilized vital signs and activity metrics measured using wearable sensors and the XGBoost algorithm for training. The proposed model described continuous recovery profiles, obtained a high predictive performance, and provided outcomes that are interpretable due to the low number of features in the final model. Moreover, activity features, the circadian rhythm of the heart, and heart rate recovery showed the highest feature importance in the recovery model. Patients could be identified with fast and slow recovery trajectories by comparing patient-specific predicted profiles to the average fast- and slow-recovering populations. This identification may facilitate determining appropriate discharge dates, detecting complications, preventing readmission, and planning physical therapy. Hence, the model can provide an automatic and objective decision support tool.

Traumatic brain injury (TBI) is a major public health concern, affecting as many as 3 million people each year in the U.S. Athletes participating in high-contact sports are at even greater risk of head injury, especially mild traumatic brain injury (mTBI) otherwise known as concussion. Physiological changes following concussion via fMRI have been a target of study since generally no changes are observed with conventional imaging such as MRI and CT. However, little is known about the hyperacute effect, defined as the first minutes after impact, as many imaging techniques are limited to time for travel and set up for scanning. Additionally, there is limited understanding of the effects of sub-concussive head impacts, which constitute a lower impact force, and no objective symptoms of a concussion. Previous concussion studies lead us to believe there may be physiological changes occurring immediately after repetitive sub- concussive impacts that are transient. Real-time, portable neuroimaging could serve as a most effective metric in monitoring sub-concussive impacts and determine brain dysfunction at the hyperacute level. The objective of these studies is to understand the immediate physiological response to the brain following sub-concussive impacts in terms of brain blood flow, oxygenation status, neurocognitive function, and balance. This was done by using two advanced portable neuroimaging techniques, i.e., transcranial Doppler (TCD) ultrasound and functional near infrared spectroscopy (fNIRS), that allow for real-time measurements following real-world head impact kinematic data captured by wearable sensor- based mouth guards. Data was collected from multiple athletes including soccer players performing multiple soccer headers, as well as mixed martial arts (MMA) athletes during sparring sessions.We found increased changes in brain oxygenation following 10 soccer headers for the soccer athletes, as well as after contact sparring sessions for the MMA athletes. Thes

Thanks to a general multidisciplinary and interdisciplinary approach, during the last few decades there have been huge advances in the diagnostic field. In particular, the miniaturization and automation of several assays have led to the development of the so-called point-of-care tests (PoCT), which are devices capable to provide accurate and specific detection of analytes such as glucose, other clinically-relevant biomarkers, pathogens, and drugs. The detection with these devices typically takes place in a few minutes and without the need of specialized personnel. Here we discuss the key technologies and applications of PoCTs, as well as the major challenges in the clinical environment.

Advances in medical technology, accessibility to health care, healthier lifestyle, diet, and hygiene have allowed us to increase life expectancy from 46 years in 1950 to almost 74 in 2019. Worldwide, the population is becoming older, and new aging-related challenges have appeared, increasing the burden on a debilitated-in-workforce healthcare system. Wearable sensors and sensor-enhanced health information systems are regarded as one way to face the implications of the aforementioned demographic change. Indeed, continuously monitoring health time series would bring detailed aspects that could potentially enable early diagnosis and an informed prognosis of any disease or injury. This dissertation presents some efforts on developing methods for injury prediction and frailty assessment from real-time health signals obtained from wearable sensors. This work was divided into three consecutive studies.In the first study, we studied an existing data set of 537 head impact kinematics in sports settings, consisting of 6 degrees of freedom measurements. We presented a principal component analysis-based method for building an accurate low-rank approximation of head impact kinematics. We found that only a few modes were sufficient for the accurate reconstruction of the entire data set. Then, we studied these representative modes in the frequency domain, which were primarily low frequency under 40Hz. We compared our approximation against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic and brain tissue deformation-based metrics. In all cases, our representation reproduced similar results in injury prediction to the ground truth. Finally, we developed a data-driven emulator capable of generating new kinematic data sets of any size, being particularly useful for training machine learning algorithms that have been investigated for approximating brain deformation in real-time for early brain injury dia

The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an appropriate performance measure has to be chosen. Our main contribution is a novel post-processing method for activity recognition. It improves the accuracy of the classification methods by correcting for unrealistic short activities in the estimate. We also propose a new performance measure, the Locally Time-Shifted Measure (LTS measure), which addresses uncertainty in the times of state changes. The effectiveness of the post-processing method is evaluated, using the novel LTS measure, on the basis of a simulated dataset and a real application on sensor data from football. The simulation study is also used to discuss the choice of the parameters of the post-processing method and the LTS measure., Statistics

Construction is known for being a labor-intensive and risky industry. Within various occupational settings such as construction, physical fatigue is an underlying health condition that may lead to musculoskeletal disorders and fall-related injuries. Identifying a worker's physical fatigue could enable safety managers to mitigate fatigue-related injuries and improve workplace operations. However, current physical fatigue assessment and identification methods include subjective, physiological, biomechanical, and computer vision approaches, which may be unreliable, intrusive, and require extensive post-processing, thus, rendering them impractical for continuous monitoring of workers' movements and automated identification of physical fatigue. Given the above, this study aims to utilize a wearable insole device to identify and classify physical fatigue levels in construction workers. Ten asymptomatic subjects were recruited to perform a fatiguing manual rebar tying activity in a laboratory setting. Borg's rating of perceived exertion (RPE) was applied as a subjective measure for collecting the levels of physical fatigue of each subject. Three sub-classification problems for identifying physical fatigue levels (i.e., PFL1, PFL2, and PFL3) were assessed. Numerous features were evaluated from the collected data samples after data segmentation. The classification performance of supervised machine learning algorithms was evaluated at a sliding window of 2.56 s. Our results from 10-fold cross-validation show an accuracy of 86% for the Random Forest (RF) algorithm, indicating the best performance among other algorithms. In addition, precision, recall, specificity, and F1-score metrics of the RF algorithm were between 52.63% and 82.62%, 52.63%–84.32%, 89.60%–92.33%, and 52.63%–83.46%, respectively. These results indicate that data samples such as acceleration and plantar pressure acquired from a wearable insole device are reliable for identifying and classifying physical fatigu

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system’s fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population’s aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask’s breathing zone in order to use the patient’s breath as a fingerprint for various diseases.

Monitoring and quantifying movement behavior is crucial for improving the health of individuals with cerebral palsy (CP). We have modeled and trained an image-based Convolutional Neural Network (CNN) to recognize specific movement classifiers relevant to individuals with CP. This study evaluates CNN’s performance and determines the feasibility of 24-h recordings. Seven sensors provided accelerometer and gyroscope data from 14 typically developed adults during videotaped physical activity. The performance of the CNN was assessed against test data and human video annotation. For feasibility testing, one typically developed adult and one adult with CP wore sensors for 24 h. The CNN demonstrated exceptional performance against test data, with a mean accuracy of 99.7%. Its general true positives (TP) and true negatives (TN) were 1.00. Against human annotators, performance was high, with mean accuracy at 83.4%, TP 0.84, and TN 0.83. Twenty-four-hour recordings were successful without data loss or adverse events. Participants wore sensors for the full wear time, and the data output were credible. We conclude that monitoring real-world movement behavior in individuals with CP is possible with multiple wearable sensors and CNN. This is of great value for identifying functional decline and informing new interventions, leading to improved outcomes., Monitoring and quantifying movement behavior is crucial for improving the health of individuals with cerebral palsy (CP). We have modeled and trained an image-based Convolutional Neural Network (CNN) to recognize specific movement classifiers relevant to individuals with CP. This study evaluates CNN’s performance and determines the feasibility of 24-h recordings. Seven sensors provided accelerometer and gyroscope data from 14 typically developed adults during videotaped physical activity. The performance of the CNN was assessed against test data and human video annotation. For feasibility testing, one typically developed adult and one adult with CP wore sensors for 24 h. The CNN demonstrated exceptional performance against test data, with a mean accuracy of 99.7%. Its general true positives (TP) and true negatives (TN) were 1.00. Against human annotators, performance was high, with mean accuracy at 83.4%, TP 0.84, and TN 0.83. Twenty-four-hour recordings were successful without data loss or adverse events. Participants wore sensors for the full wear time, and the data output were credible. We conclude that monitoring real-world movement behavior in individuals with CP is possible with multiple wearable sensors and CNN. This is of great value for identifying functional decline and informing new interventions, leading to improved outcomes.