Physical fatigue detection through EMG wearables and subjective user reports-Reference-Cited by-同舟云学术

Physical fatigue detection through EMG wearables and subjective user reports

Published:2019-06-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments
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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

Link

https://dl.acm.org/doi/pdf/10.1145/3316782.3322772

Reference12 articles.

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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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