Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System
Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user’s intention to move or muscle fatigue. These issues impede the user’s abil...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2377 |
| _version_ | 1827286535402160128 |
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| author | Wenbo Zhang Ziqian Bai Pengfei Yan Hongwei Liu Li Shao |
| author_facet | Wenbo Zhang Ziqian Bai Pengfei Yan Hongwei Liu Li Shao |
| author_sort | Wenbo Zhang |
| collection | DOAJ |
| description | Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user’s intention to move or muscle fatigue. These issues impede the user’s ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status. |
| first_indexed | 2024-04-24T10:35:06Z |
| format | Article |
| id | doaj.art-e81cf669fbef4ef98e5e5362e9669906 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:35:06Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e81cf669fbef4ef98e5e5362e96699062024-04-12T13:26:55ZengMDPI AGSensors1424-82202024-04-01247237710.3390/s24072377Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG SystemWenbo Zhang0Ziqian Bai1Pengfei Yan2Hongwei Liu3Li Shao4School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaFunctional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user’s intention to move or muscle fatigue. These issues impede the user’s ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status.https://www.mdpi.com/1424-8220/24/7/2377functional electrical stimulationsurface electromyographyhuman motion recognitionmuscle fatigue status |
| spellingShingle | Wenbo Zhang Ziqian Bai Pengfei Yan Hongwei Liu Li Shao Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System Sensors functional electrical stimulation surface electromyography human motion recognition muscle fatigue status |
| title | Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System |
| title_full | Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System |
| title_fullStr | Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System |
| title_full_unstemmed | Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System |
| title_short | Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System |
| title_sort | recognition of human lower limb motion and muscle fatigue status using a wearable fes semg system |
| topic | functional electrical stimulation surface electromyography human motion recognition muscle fatigue status |
| url | https://www.mdpi.com/1424-8220/24/7/2377 |
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