Grasping force estimation for prosthetic hands via feature extraction of surface EMG
Lisha Xu, Gaoke Zhu, Xiaogang Duan
Available Online November 2016.
- https://doi.org/10.2991/aest-16.2016.23How to use a DOI?
- force estimation; feature extraction; surface EMG; prosthetic hands.
- A prosthetic hand with a self-regulated grip force could achieve different operation modes, which can help the upper limb amputees to fetch objects of different shapes. To get the appropriate grasping force with smaller samples and shorter training time, the method of threshold value judgment in this paper is effective on achieving the estimate of the discrete force basing on the mean absolute value (MAV) of EMG's level. The 10 subjects can be divided into 8 grasping patterns determined through three levels: the small, medium and great of grasping forces in experiments. Experimental results conditioned on small training samples and short training time show that the accuracy of force estimation is 72.91±9.58% and thereby convincing the effectiveness and reality of the proposed method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lisha Xu AU - Gaoke Zhu AU - Xiaogang Duan PY - 2016/11 DA - 2016/11 TI - Grasping force estimation for prosthetic hands via feature extraction of surface EMG BT - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016) PB - Atlantis Press SP - 177 EP - 185 SN - 1951-6851 UR - https://doi.org/10.2991/aest-16.2016.23 DO - https://doi.org/10.2991/aest-16.2016.23 ID - Xu2016/11 ER -