Comparison of sEMG bit-stream modulators for cross-correlation based muscle fatigue estimation

Author(s):  
Daiwen Sun ◽  
Ermis Koutsos ◽  
Pantelis Georgiou
2011 ◽  
pp. 467-477
Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.


2011 ◽  
Vol 21 (2) ◽  
pp. 236-241 ◽  
Author(s):  
Mehran Talebinejad ◽  
Adrian D.C. Chan ◽  
Ali Miri

Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 191751-191757
Author(s):  
Inyeol Yun ◽  
Jinpyeo Jeung ◽  
Yonghun Song ◽  
Yoonyoung Chung

2016 ◽  
Vol 88 (5-8) ◽  
pp. 1523-1533 ◽  
Author(s):  
Jingtao Chen ◽  
Peter Mitrouchev ◽  
Sabine Coquillart ◽  
Franck Quaine

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Nurul Asyikin Kamaruddin ◽  
Puspa Inayat Khalid ◽  
Ahmad Zuri Shaameri

The developments in physiological studies have established the importance of muscle fatigue estimation in various aspects including neurophysiological and medical research, rehabilitation, ergonomics, sports injuries and human-computer interaction. Surface electromyography signals are commonly used in muscle fatigue assessment. Techniques of surface EMG signal processing used to quantify muscle fatigue are not only based on time domain and frequency domain, but also on time–frequency domain. The developments of different signal analysis to extract different indices for muscle fatigue assessments are reviewed in this paper. Several indices in time, frequency, and time-frequency representations for muscle fatigue assessments have been identified. However the sensitivity of those indices needs to be investigated. Minimizing this issue becomes the objective of the recent research in muscle fatigue assessments.


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