Wearable Sensors Applied in Movement Analysis
Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processin...
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| Формат: | Online |
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| Язык: | английский |
| Опубликовано: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Предметы: | |
| Online-ссылка: | ONIX_20221206_9783036558608_113 |
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Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869531527962951680 |
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| collection | Directory of Open Access Books |
| description | Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges. |
| format | Online |
| id | doab-20.500.12854ir-94590 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-945902024-03-31T13:08:52Z Wearable Sensors Applied in Movement Analysis Buisseret, Fabien Dierick, Frédéric Van der Perre, Liesbet inertial measurement unit movement analysis long-track speed skating validity IMU principal component analysis wearable scoring carving balance assessment data augmentation gated recurrent unit human activity recognition one-dimensional convolutional neural network intermittent claudication vascular rehabilitation 6 min walking test functional walking TUG kinematics fall risk logistic regression elderly inertial sensor artificial intelligence supervised machine learning head rotation test neck pain cerebral palsy dystonia choreoathetosis machine learning home-based wearable device MLP gesture recognition flex sensor model search neural network inertial measurement unit—IMU movement complexity sample entropy trunk flexion low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Bayesian neural network pain self-efficacy questionnaire n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges. 2022-12-06T16:13:16Z 2022-12-06T16:13:16Z 2022 book ONIX_20221206_9783036558608_113 9783036558608 9783036558592 https://directory.doabooks.org/handle/20.500.12854/94590 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6426 https://mdpi.com/books/pdfview/book/6426 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5859-2 10.3390/books978-3-0365-5859-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036558608 9783036558592 154 Basel open access |
| spellingShingle | inertial measurement unit movement analysis long-track speed skating validity IMU principal component analysis wearable scoring carving balance assessment data augmentation gated recurrent unit human activity recognition one-dimensional convolutional neural network intermittent claudication vascular rehabilitation 6 min walking test functional walking TUG kinematics fall risk logistic regression elderly inertial sensor artificial intelligence supervised machine learning head rotation test neck pain cerebral palsy dystonia choreoathetosis machine learning home-based wearable device MLP gesture recognition flex sensor model search neural network inertial measurement unit—IMU movement complexity sample entropy trunk flexion low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Bayesian neural network pain self-efficacy questionnaire n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques Wearable Sensors Applied in Movement Analysis |
| title | Wearable Sensors Applied in Movement Analysis |
| title_full | Wearable Sensors Applied in Movement Analysis |
| title_fullStr | Wearable Sensors Applied in Movement Analysis |
| title_full_unstemmed | Wearable Sensors Applied in Movement Analysis |
| title_short | Wearable Sensors Applied in Movement Analysis |
| title_sort | wearable sensors applied in movement analysis |
| topic | inertial measurement unit movement analysis long-track speed skating validity IMU principal component analysis wearable scoring carving balance assessment data augmentation gated recurrent unit human activity recognition one-dimensional convolutional neural network intermittent claudication vascular rehabilitation 6 min walking test functional walking TUG kinematics fall risk logistic regression elderly inertial sensor artificial intelligence supervised machine learning head rotation test neck pain cerebral palsy dystonia choreoathetosis machine learning home-based wearable device MLP gesture recognition flex sensor model search neural network inertial measurement unit—IMU movement complexity sample entropy trunk flexion low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Bayesian neural network pain self-efficacy questionnaire n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques |
| topic_facet | inertial measurement unit movement analysis long-track speed skating validity IMU principal component analysis wearable scoring carving balance assessment data augmentation gated recurrent unit human activity recognition one-dimensional convolutional neural network intermittent claudication vascular rehabilitation 6 min walking test functional walking TUG kinematics fall risk logistic regression elderly inertial sensor artificial intelligence supervised machine learning head rotation test neck pain cerebral palsy dystonia choreoathetosis machine learning home-based wearable device MLP gesture recognition flex sensor model search neural network inertial measurement unit—IMU movement complexity sample entropy trunk flexion low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Bayesian neural network pain self-efficacy questionnaire n/a thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques |
| url | ONIX_20221206_9783036558608_113 |