Application Of Digital Signal Processing And Machine Learning For Electromyography: A Review
Digital signal processing (DSP) and Machine learning (ML) have emerged as promising approaches to automate prediction tasks into electromyography (EMG) muscles conditions. To fill the research gap, This paper reviews the state-of-the-art applications of DSP and ML for EMG signal analysis. DSP techni...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AJMedTech
2021
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/25652/2/5-FULL%20ARTICLE%20-84-1-10-20210730%20AJMEDTECH.PDF http://eprints.utem.edu.my/id/eprint/25652/ https://ajmedtech.com/index.php/journal/article/view/5/3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | Digital signal processing (DSP) and Machine learning (ML) have emerged as promising approaches to automate prediction tasks into electromyography (EMG) muscles conditions. To fill the research gap, This paper reviews the state-of-the-art applications of DSP and ML for EMG signal analysis. DSP techniques to extract information of EMG signal is highly needed. The major disadvantage of the frequency domain approach is it does not represent temporal information. Many time-frequency analysis techniques have been proposed. However, there is a compromise between time and frequency resolution. The techniques that minimize the EMG noise and analyze signal characteristics are discussed together to identify the best performance with the highest percentage of accuracy and efficiency. The most appropriate method depends on the EMG signal patterns, the quality and quantity of the signals and training data developed, and various types of user factors. |
|---|