ArticleSensors (Basel, Switzerland)2024
Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- [Cluster-guided adaptive Transformer for muscle fatigue prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Subject-independent gait activity recognition using DSAF: dual-stream IMU-EMG attention fusion with asymmetric temporal encoding and physiological complementarity weighting.Frontiers in neurorobotics · 2026Article
- The fatigue status feature of bicycle movement based on deep learning and signal processing technology.Scientific reports · 2025Article
- Detecting muscle fatigue during lower limb isometric contractions tasks: a machine learning approach.Frontiers in physiology · 2025Article
- One-Dimensional Motion Representation for Standing/Sitting and Their Transitions.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
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Abstract
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.
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Registered trials
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