Fingertip force coordination is crucial to the success of grasp-and-lift tasks. In the development of motor prosthesis for daily applications, the ability to accurately classify the desired grasp-and-lift from multi-channel surface electromyography (sEMG) is essential. In order to extract reliable indicators for fingertip force coordination, we searched an extensive set of sEMG features for the optimal subset of relevant features. Using mutual information based feature selection we found that a subset of not more than 10 sEMG features selected from over seven thousand, could effectively classify object weights in grasp-and-lift tasks. Average classification accuracies of 82.53% in the acceleration phase and 88.61% in the isometric contraction phase were achieved. Furthermore, sEMG features associated with object weights and common across individuals were identified. These time-domain features (entropy, mean/median absolute deviation, pNNx) can be calculated efficiently, providing possible new indicators.
|Title of host publication||2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society|
|Subtitle of host publication||Smarter Technology for a Healthier World, EMBC 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|Publication status||Published - 13 Sept 2017|
|Event||39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of|
Duration: 11 Jul 2017 → 15 Jul 2017
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017|
|Country/Territory||Korea, Republic of|
|Period||11/07/17 → 15/07/17|
Bibliographical noteFunding Information:
*This work was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU110813].
© 2017 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics