Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine le...

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Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering Jg. 28; H. 12; S. 2933 - 2943
Hauptverfasser:	Perez-Ibarra, Juan C., Siqueira, Adriano A. G., Krebs, Hermano I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Algorithms Automatic control Foot Gait Gait analysis hidden Markov model Hidden Markov models human biomechanics Inertial sensing devices Learning algorithms Legged locomotion Machine learning Mechanical engineering Movement disorders Neurodegenerative diseases Parameter modification Parkinson's disease Pathology Real time Real-time systems Resistors robotic rehabilitation Sensors Training Treadmills Unsupervised learning Walking wearable sensors Wearable technology
ISSN:	1534-4320, 1558-0210, 1558-0210
Online-Zugang:	Volltext
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