Moniteur d'activité physique

Résumé

thumb|Des bracelets connectés Samsung Galaxy Fit. Un moniteur d'activité physique, aussi appelé bracelet connecté ou traqueur d'activité (en anglais, activity tracker) est un appareil électronique ou une application qui permet de mesurer l'intensité et la quantité d'activité physique effectuée par un individu tels que la distance marchée ou courue, la consommation et la dépense de calories, et dans certains cas le rythme cardiaque et la qualité du sommeil. Le but du moniteur est d'augmenter la motivation de son utilisateur et de lui permettre d'améliorer ses performances. Le terme est maintenant utilisé principalement pour désigner des appareils électroniques de surveillance dédiés qui sont synchronisés, dans de nombreux cas sans fil, à un ordinateur ou à un téléphone intelligent pour le suivi des données à long terme. Il s'agit alors d'un exemple de technologie portable et d'objet connecté. Le terme est aussi utilisé pour désigner des applications sur téléphones intelligents et des

Source officielle

https://fr.wikipedia.org/wiki/Moniteur_d'activit%C3%A9_physique

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Modelling, Simulations and Characterization of NEMS accelerometers based on graphene

Daniel Moreno Garcia

Accelerometers are widely used in industrial applications and consumer electronics. We can find them in automotive crash detection or fitness trackers. The majority are based on piezoresistive or capacitive effect which are limited by their large size. This negatively influences their utility in emerging applications such as Internet of Things and biomedical applications. NEMS (Nanoelectromechanical systems) are presented as a possible solution for this problem. The accelerometers used in this project are made of a suspended graphene membrane and an attached silicon proof mass. They were fabricated by KTH researchers. We study the possibility of measuring accelerations by monitoring the changes in the device resonant frequency. If proven, to the best of our knowledge, they would be the first ultra-miniaturized and sensitive resonant accelerometer. To do so, we built a functioning model of the accelerometers by combining a theoretical framework and finite element simulations. The chip was placed on a piezo-shaker and measurements were conducted using a Laser Doppler Vibrometer. To complement the study, further measures were taken with an Atomic Force Microscope and a Digital Holographic Microscope. The results show a good match between theory and simulations. However, the acceleration measurements in the device show higher signals not related with accelerations. We proved they were related to displacements and theorized that they were caused by internal chip forces. Horizontal forces on the accelerometer frame could eclipse the effect of the accelerations on the resonant frequency. We suggest the use of a more rigid chip frame as a potential solution.

2020

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Instrumented shoes for daily activity monitoring in healthy and at risk populations

Christopher Moufawad El Achkar

Daily activity reflects the health status of an individual. Ageing and disease drastically affect all dimensions of mobility, from the number of active bouts to their duration and intensity. Performing less activity leads to muscle deterioration and further weakness that could lead to increased fall risk. Gait performance is also affected by ageing and could be detrimental for daily mobility. Therefore, activity monitoring in older adults and at risk persons is crucial to obtain relevant quantitative information about daily life performance. Activity evaluation has mainly been established through questionnaires or daily logs. These methods are simple but not sufficiently accurate and are prone to errors. With the advent of microelectromechanical systems (MEMS), the availability of wearable sensors has shifted activity analysis towards ambulatory monitoring. In particular, inertial measurement units consisting of accelerometers and gyroscopes have shown to be extremely relevant for characterizing human movement. However, monitoring daily activity requires comfortable and easy to use systems that are strategically placed on the body or integrated in clothing to avoid movement hindrance. Several research based systems have employed multiple sensors placed at different locations, capable of recognizing activity types with high accuracy, but not comfortable for daily use. Single sensor systems have also been used but revealed inaccuracies in activity recognition. To this end, we propose an instrumented shoe system consisting of an inertial measurement unit and a pressure sensing insole with all the sensors placed at the shoe/foot level. By measuring the foot movement and loading, the recognition of locomotion and load bearing activities would be appropriate for activity classification. Furthermore, inertial measurement units placed on the foot can perform detailed gait analysis, providing the possibility of characterizing locomotion. The system and dedicated activity classification algorithms were first designed, tested and validated during the first part of the thesis. Their application to clinical rehabilitation of at risk persons was demonstrated over the second part. In the first part of the thesis, the designed instrumented shoes system was tested in standardized conditions with healthy elderly subjects performing a sequence of structured activities. An algorithm based on movement biomechanics was built to identify each activity, namely sitting, standing, level walking, stairs, ramps, and elevators. The rich array of sensors present in the system included a 3D accelerometer, 3D gyroscope, 8 force sensors, and a barometer allowing the algorithm to reach a high accuracy in classifying different activity types. The tuning parameters of the algorithm were shown to be robust to small changes, demonstrating the suitability of the algorithm to activity classification in older adults. Next, the system was tested in daily life conditions on the same elderly participants. Using a wearable reference system, the concurrent validity of the instrumented shoes in classifying daily activity was shown. Additionally, daily gait metrics were obtained and compared to the literature. Further insight into the relationship between some gait parameters as well as a global activity metric, the activity âcomplexityâ, was discussed. Participants positively rated their comfort while using the system... (Please refer to thesis for full abstract)

EPFL2016

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Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults.

Mdpi Ag2016

Chargement

Instrumented shoes for daily activity monitoring in healthy and at risk populations

Christopher Moufawad El Achkar

EPFL2016