Physical fatigue detection through EMG wearables and subjective user reports

Author:

Papakostas Michalis1,Kanal Varun1,Abujelala Maher1,Tsiakas Konstantinos2,Makedon Fillia1

Affiliation:

1. University of Texas at Arlington

2. Yale University, Hew Haven, Connecticut

Funder

National Science Foundation

Publisher

ACM

Reference12 articles.

1. Robot-aided rehabilitation using force analysis

2. Ekaterina Dobryakova Helen M Genova John DeLuca and Glenn R Wylie. 2015. The dopamine imbalance hypothesis of fatigue in multiple sclerosis and other neurological disorders. Frontiers in neurology 6 (2015) 52. Ekaterina Dobryakova Helen M Genova John DeLuca and Glenn R Wylie. 2015. The dopamine imbalance hypothesis of fatigue in multiple sclerosis and other neurological disorders. Frontiers in neurology 6 (2015) 52.

3. PA Karthick Diptasree Maitra Ghosh and S Ramakrishnan. 2018. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Computer methods and programs in biomedicine 154 (2018) 45--56. PA Karthick Diptasree Maitra Ghosh and S Ramakrishnan. 2018. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms. Computer methods and programs in biomedicine 154 (2018) 45--56.

4. Towards predicting task performance from EEG signals

5. Sang-Hui Park and Seok-Pil Lee. 1998. EMG pattern recognition based on artificial intelligence techniques. IEEE transactions on Rehabilitation Engineering 6 4 (1998) 400--405. Sang-Hui Park and Seok-Pil Lee. 1998. EMG pattern recognition based on artificial intelligence techniques. IEEE transactions on Rehabilitation Engineering 6 4 (1998) 400--405.

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