A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users
With the elderly and disabled population increasing worldwide, the functionalities of smart wheelchairs as mobility assistive equipment are becoming more enriched and extended. Although there is a well-established body of literature on fatigue detection methods and systems, fatigue detection for whe...
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doaj-2b8cf9d1bf8b41bb9f29d043ee6029682021-03-29T20:13:29ZengIEEEIEEE Access2169-35362017-01-015194201943110.1109/ACCESS.2017.27309208000554A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair UsersWenfeng Li0Xinyun Hu1https://orcid.org/0000-0002-1162-5095Raffaele Gravina2Giancarlo Fortino3School of Logistics Engineering, Wuhan University of Technology, Wuhan, ChinaSchool of Logistics Engineering, Wuhan University of Technology, Wuhan, ChinaDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, ItalyDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, ItalyWith the elderly and disabled population increasing worldwide, the functionalities of smart wheelchairs as mobility assistive equipment are becoming more enriched and extended. Although there is a well-established body of literature on fatigue detection methods and systems, fatigue detection for wheelchair users has still not been widely explored. This paper proposes a neuro-fuzzy fatigue tracking and classification system and applies this method to classify fatigue degree for manual wheelchair users. In the proposed system, physiological and kinetic data are collected, including surface electromyography, electrocardiography, and acceleration signals. The necessary features are then extracted from the signals and integrated with a self-rating method to train the neuro-fuzzy classifier. Four degrees of fatigue status can be distinguished to provide further fatigue and alertness prediction in the event of musculoskeletal disorders caused by underlying fatigue.https://ieeexplore.ieee.org/document/8000554/FatigueECGEMGneuro-fuzzy classifierbody sensor networksmart wheelchair |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenfeng Li Xinyun Hu Raffaele Gravina Giancarlo Fortino |
spellingShingle |
Wenfeng Li Xinyun Hu Raffaele Gravina Giancarlo Fortino A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users IEEE Access Fatigue ECG EMG neuro-fuzzy classifier body sensor network smart wheelchair |
author_facet |
Wenfeng Li Xinyun Hu Raffaele Gravina Giancarlo Fortino |
author_sort |
Wenfeng Li |
title |
A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users |
title_short |
A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users |
title_full |
A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users |
title_fullStr |
A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users |
title_full_unstemmed |
A Neuro-Fuzzy Fatigue-Tracking and Classification System for Wheelchair Users |
title_sort |
neuro-fuzzy fatigue-tracking and classification system for wheelchair users |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2017-01-01 |
description |
With the elderly and disabled population increasing worldwide, the functionalities of smart wheelchairs as mobility assistive equipment are becoming more enriched and extended. Although there is a well-established body of literature on fatigue detection methods and systems, fatigue detection for wheelchair users has still not been widely explored. This paper proposes a neuro-fuzzy fatigue tracking and classification system and applies this method to classify fatigue degree for manual wheelchair users. In the proposed system, physiological and kinetic data are collected, including surface electromyography, electrocardiography, and acceleration signals. The necessary features are then extracted from the signals and integrated with a self-rating method to train the neuro-fuzzy classifier. Four degrees of fatigue status can be distinguished to provide further fatigue and alertness prediction in the event of musculoskeletal disorders caused by underlying fatigue. |
topic |
Fatigue ECG EMG neuro-fuzzy classifier body sensor network smart wheelchair |
url |
https://ieeexplore.ieee.org/document/8000554/ |
work_keys_str_mv |
AT wenfengli aneurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT xinyunhu aneurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT raffaelegravina aneurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT giancarlofortino aneurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT wenfengli neurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT xinyunhu neurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT raffaelegravina neurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers AT giancarlofortino neurofuzzyfatiguetrackingandclassificationsystemforwheelchairusers |
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