LEADER 04172nam 2200985z- 450 001 9910637794103321 005 20231214132956.0 010 $a3-0365-5859-4 035 $a(CKB)5470000001631595 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94590 035 $a(EXLCZ)995470000001631595 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aWearable Sensors Applied in Movement Analysis 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (154 p.) 311 $a3-0365-5860-8 330 $aRecent 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. 606 $aMedical equipment & techniques$2bicssc 610 $ainertial measurement unit 610 $amovement analysis 610 $along-track speed skating 610 $avalidity 610 $aIMU 610 $aprincipal component analysis 610 $awearable 610 $ascoring 610 $acarving 610 $abalance assessment 610 $adata augmentation 610 $agated recurrent unit 610 $ahuman activity recognition 610 $aone-dimensional convolutional neural network 610 $aintermittent claudication 610 $avascular rehabilitation 610 $a6 min walking test 610 $afunctional walking 610 $aTUG 610 $akinematics 610 $afall risk 610 $alogistic regression 610 $aelderly 610 $ainertial sensor 610 $aartificial intelligence 610 $asupervised machine learning 610 $ahead rotation test 610 $aneck pain 610 $acerebral palsy 610 $adystonia 610 $achoreoathetosis 610 $amachine learning 610 $ahome-based 610 $awearable device 610 $aMLP 610 $agesture recognition 610 $aflex sensor 610 $amodel search 610 $aneural network 610 $ainertial measurement unit-IMU 610 $amovement complexity 610 $asample entropy 610 $atrunk flexion 610 $alow back pain 610 $alifting technique 610 $acamera system 610 $award clustering method 610 $aK-means clustering method 610 $aensemble clustering method 610 $aBayesian neural network 610 $apain self-efficacy questionnaire 615 7$aMedical equipment & techniques 700 $aBuisseret$b Fabien$4edt$01290155 702 $aDierick$b Fre?de?ric$4edt 702 $aVan der Perre$b Liesbet$4edt 702 $aBuisseret$b Fabien$4oth 702 $aDierick$b Fre?de?ric$4oth 702 $aVan der Perre$b Liesbet$4oth 906 $aBOOK 912 $a9910637794103321 996 $aWearable Sensors Applied in Movement Analysis$93021362 997 $aUNINA