Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism

Avian, Cries; Prakosa, Setya Widyawan; Faisal, Muhamad; Leu, Jenq-Shiou

doi:10.1016/j.bspc.2021.103099

Cited by 15 publications

(2 citation statements)

References 51 publications

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“…It is essential for obtaining accurate measurements of muscle activation patterns and identifying changes in muscle function during physical exertion. Other methods used for preprocessing include Independent Component Analysis (ICA) and empirical mode decomposition (EMD) [104], Ensemble Empirical Mode Decomposition (EEMD) with Hilbert Transform (HT) [105], and Discrete Wavelet Transform (DWT) [106]. In estimating muscle activity onsets, methods such as visual and automated methods [107], sample entropy (SampEn) analysis [108], and sequential Gaussian mixture model (GMM) have been proposed [109].…”

Section: Methods For Connecting Sensors To Intelligent Garment Systemmentioning

confidence: 99%

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Shen,

Tao,

Koncar

et al. 2023

IEEE Access

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Bioelectric signals are significant indicators of the state of health of the human body, especially in sports monitoring, where athletes' fatigue state and performance need to be monitored in realtime to develop a proper training plan. Due to the characteristics of sports, it is difficult to obtain the dynamic bioelectrical signals of the human body during exercise. This paper provides a comprehensive overview of the current knowledge on Intelligent Garment Systems (IGS) for long-lasting bioelectric monitoring in sports. This review includes a detailed examination of human bioelectric signals, focusing on ECG, EMG, and GSR signals and their applications in intelligent wearable technologies. The definition and development history of IGS is also discussed, along with a review of the primary research components of IGS, including dry textile electrodes, methods for connecting sensors to IGS, and processing methods for bioelectric signals. The paper concludes by highlighting the current challenges faced by IGS in terms of real-time dynamic monitoring and connection problems and outlining the future directions for this field, including the need for further advancements in bioelectric signal processing and analysis, the development of new materials and connection technologies, and the integration of artificial intelligence and machine learning into IGS.

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Section: Methods For Connecting Sensors To Intelligent Garment Systemmentioning

confidence: 99%

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Shen,

Tao,

Koncar

et al. 2023

IEEE Access

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“… Zhang et al (2019) decomposed sEMG signals by principal component analysis (PCA) into principal components and weight vectors that improve the validity of parameters. In Avian et al (2022) , Discrete Wavelet Transform (DWT) is used to process the sEMG signal to increase model performance.…”

Section: Acquisition Of Surface Electromyography and Preprocessingmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

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Continuous motion finger joint angle estimation utilizing hybrid sEMG-FMG modality driven transformer-based deep learning model

Chen

Wang

Chen

et al. 2023

Biomedical Signal Processing and Control

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Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism

Cited by 15 publications

References 51 publications

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Application of Surface Electromyography in Exercise Fatigue: A Review

Continuous motion finger joint angle estimation utilizing hybrid sEMG-FMG modality driven transformer-based deep learning model

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