Abstract
Myoelectric control with surface EMG signal has achieved great success in clinics, but only limited to the control of 2-Degrees-of-freedom prosthesis. With the appearance of multiple-channel and high-density EMG system and the advances
of pattern recognition technology, it becomes possible to control a multi-degree smart prosthesis using EMG signals. However, it requires high performance EMG systems with high sampling frequency, which impedes the popularity of EMG-based applications. This study aims to explore a way to reduce the cost of EMG system by investigating the effect of sampling rate on gesture recognition accuracy. Two groups of experiments on inner-group and cross-group were designed to evaluate the classification accuracy at different EMG sampling frequency. In comparison with the sampling frequency at 1kHz, a lower sampling frequency at 400 Hz could achieve comparable accuracy, reduced by only 0.43% (KNN) and 0.83% (SVM) with the overall accuracy at 99.40% and 98.67%, respectively. It implies that appropriate reduction of the sampling frequency can be a good choice to balance the cost and performance of a multiple channel EMG system for feature-based hand gesture classification.
of pattern recognition technology, it becomes possible to control a multi-degree smart prosthesis using EMG signals. However, it requires high performance EMG systems with high sampling frequency, which impedes the popularity of EMG-based applications. This study aims to explore a way to reduce the cost of EMG system by investigating the effect of sampling rate on gesture recognition accuracy. Two groups of experiments on inner-group and cross-group were designed to evaluate the classification accuracy at different EMG sampling frequency. In comparison with the sampling frequency at 1kHz, a lower sampling frequency at 400 Hz could achieve comparable accuracy, reduced by only 0.43% (KNN) and 0.83% (SVM) with the overall accuracy at 99.40% and 98.67%, respectively. It implies that appropriate reduction of the sampling frequency can be a good choice to balance the cost and performance of a multiple channel EMG system for feature-based hand gesture classification.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 978-1538616451 |
| ISBN (Print) | 978-1538616468 |
| DOIs | |
| Publication status | Published - 1 Dec 2017 |
| Event | 2017 IEEE Conference on Systems, Man, and Cybernetics - Banff, Alberta, Canada Duration: 5 Oct 2017 → 8 Oct 2017 |
Conference
| Conference | 2017 IEEE Conference on Systems, Man, and Cybernetics |
|---|---|
| Abbreviated title | SMC 2017 |
| Country/Territory | Canada |
| City | Banff, Alberta |
| Period | 5/10/17 → 8/10/17 |
Keywords
- surface EMG
- sampling rate
- prosthesis
- hand motion
- pattern recognition