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

Fang Peng, Wei Peng, Cheng Zhang, Debao Zhong

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number8708273
Pages (from-to)68240-68249
Number of pages10
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Discriminant analysis
Electromyography
Fusion reactions
Servers
Robots
Internet of things
Sensors
Experiments

Keywords

  • classifier
  • gait phase recognition
  • IoT
  • kernel LDA
  • Surface electromyography (sEMG)

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking with Cognitive Tasks. / Peng, Fang; Peng, Wei; Zhang, Cheng; Zhong, Debao.

In: IEEE Access, Vol. 7, 8708273, 01.01.2019, p. 68240-68249.

Research output: Contribution to journalArticle

@article{0936944152794be2a82b57db948229dc,
title = "IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking with Cognitive Tasks",
abstract = "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.",
keywords = "classifier, gait phase recognition, IoT, kernel LDA, Surface electromyography (sEMG)",
author = "Fang Peng and Wei Peng and Cheng Zhang and Debao Zhong",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/ACCESS.2019.2915290",
language = "English",
volume = "7",
pages = "68240--68249",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking with Cognitive Tasks

AU - Peng, Fang

AU - Peng, Wei

AU - Zhang, Cheng

AU - Zhong, Debao

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - classifier

KW - gait phase recognition

KW - IoT

KW - kernel LDA

KW - Surface electromyography (sEMG)

UR - http://www.scopus.com/inward/record.url?scp=85067189543&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067189543&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2019.2915290

DO - 10.1109/ACCESS.2019.2915290

M3 - Article

VL - 7

SP - 68240

EP - 68249

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8708273

ER -