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...

Full description

Saved in:
Bibliographic Details
Other Authors: Buisseret, Fabien (Editor), Dierick, Frédéric (Editor), Van der Perre, Liesbet (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:DOAB: download the publication
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000naaaa2200000uu 4500
001 doab_20_500_12854_94590
005 20221206
003 oapen
006 m o d
007 cr|mn|---annan
008 20221206s2022 xx |||||o ||| 0|eng d
020 |a books978-3-0365-5859-2 
020 |a 9783036558608 
020 |a 9783036558592 
040 |a oapen  |c oapen 
024 7 |a 10.3390/books978-3-0365-5859-2  |c doi 
041 0 |a eng 
042 |a dc 
072 7 |a MBG  |2 bicssc 
100 1 |a Buisseret, Fabien  |4 edt 
700 1 |a Dierick, Frédéric  |4 edt 
700 1 |a Van der Perre, Liesbet  |4 edt 
700 1 |a Buisseret, Fabien  |4 oth 
700 1 |a Dierick, Frédéric  |4 oth 
700 1 |a Van der Perre, Liesbet  |4 oth 
245 1 0 |a Wearable Sensors Applied in Movement Analysis 
260 |a Basel  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2022 
300 |a 1 electronic resource (154 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a 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. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Medical equipment & techniques  |2 bicssc 
653 |a inertial measurement unit 
653 |a movement analysis 
653 |a long-track speed skating 
653 |a validity 
653 |a IMU 
653 |a principal component analysis 
653 |a wearable 
653 |a scoring 
653 |a carving 
653 |a balance assessment 
653 |a data augmentation 
653 |a gated recurrent unit 
653 |a human activity recognition 
653 |a one-dimensional convolutional neural network 
653 |a intermittent claudication 
653 |a vascular rehabilitation 
653 |a 6 min walking test 
653 |a functional walking 
653 |a TUG 
653 |a kinematics 
653 |a fall risk 
653 |a logistic regression 
653 |a elderly 
653 |a inertial sensor 
653 |a artificial intelligence 
653 |a supervised machine learning 
653 |a head rotation test 
653 |a neck pain 
653 |a cerebral palsy 
653 |a dystonia 
653 |a choreoathetosis 
653 |a machine learning 
653 |a home-based 
653 |a wearable device 
653 |a MLP 
653 |a gesture recognition 
653 |a flex sensor 
653 |a model search 
653 |a neural network 
653 |a inertial measurement unit-IMU 
653 |a movement complexity 
653 |a sample entropy 
653 |a trunk flexion 
653 |a low back pain 
653 |a lifting technique 
653 |a camera system 
653 |a ward clustering method 
653 |a K-means clustering method 
653 |a ensemble clustering method 
653 |a Bayesian neural network 
653 |a pain self-efficacy questionnaire 
653 |a n/a 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/6426  |7 0  |z DOAB: download the publication 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/94590  |7 0  |z DOAB: description of the publication