Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction

Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm buil...

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Veröffentlicht in:	Annals of biomedical engineering Jg. 51; H. 7; S. 1471 - 1484
Hauptverfasser:	Yu, Shuangyue, Yang, Jianfu, Huang, Tzu-Hao, Zhu, Junxi, Visco, Christopher J., Hameed, Farah, Stein, Joel, Zhou, Xianlian, Su, Hao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.07.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Biochemistry Biological and Medical Physics Biomechanical Phenomena Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Classical Mechanics Controllers Gait Humans Lower Extremity Movement Disorders Neural networks Neural Networks, Computer Original Article Real time Thigh Walking Wearable computers Wearable technology Gait phase detection Exoskeleton Activities classification Artificial neural networks
ISSN:	0090-6964, 1573-9686, 1573-9686
Online-Zugang:	Volltext
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