Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependen...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 4; S. 2153
Hauptverfasser:	Yan, Jianjun, Wang, Xueqiang, Shi, Jiangtao, Hu, Shuai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 14.02.2023 MDPI
Schlagworte:	Accelerometers Accidental Falls Accuracy Activities of Daily Living Algorithms Datasets fall detection Human influences Human mechanics Humans Machine learning multiple inertial sensors Musculoskeletal System Neural networks Older people Parkinson's disease Performance evaluation Privacy Reproducibility of Results Sensors Skeleton spatial-temporal graph convolutional networks Support vector machines Wearable computers spatial-temporal graph convolutional networks skeleton fall detection multiple inertial sensors
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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