IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking With Cognitive Tasks

Gait phase detection is an important procedure in the application of many lower limb auxiliary robots. The gait phase detection algorithms that utilize surface electromyography (sEMG) signals have been developed to overcome unnatural walking. However, traditional studies mostly consider precise gait...

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Vydáno v:IEEE access Ročník 7; s. 68240 - 68249
Hlavní autoři: Peng, Fang, Peng, Wei, Zhang, Cheng, Zhong, Debao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
classifier
Cognitive tasks
Discriminant analysis
Electromyography
Error reduction
Feature extraction
gait phase recognition
Gait recognition
Internet of Things
IoT
Kernel
kernel LDA
Kernels
Legged locomotion
Nonlinear analysis
Phase detection
Sensors
Surface electromyography (sEMG)
Task analysis
Walking
ISSN:2169-3536, 2169-3536
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Shrnutí:Gait phase detection is an important procedure in the application of many lower limb auxiliary robots. The gait phase detection algorithms that utilize surface electromyography (sEMG) signals have been developed to overcome unnatural walking. However, traditional studies mostly consider precise gait walking events when the subject is focusing only on walking, and the accuracy of traditional algorithms is susceptible to more realistic gait scenarios, such as walking with cognitive tasks. In this paper, the gait phase detection is considered in more realistic and challenging scenarios, in which the subject is walking while performing cognitive tasks. A kernel linear discriminant analysis (LDA)-based nonlinear fusion model is proposed for gait recognition, which can effectively reduce the error caused by cognitive tasks, making it an ideal model for gait phase detection while cognitive tasks are performed in the process of walking. Furthermore, the Internet of Things (IoT) framework is incorporated to reduce the gait phase detection algorithm's process time by offloading the data from local sEMG sensors to the IoT server with powerful computation capability. The experiments have been conducted to validate our proposed algorithms, demonstrating that the boundaries between the stance period and swing period are more blurred when walking with cognitive tasks.
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2915290