Application of muscle redistribution in the signal recognition of an intelligent bionic prosthesis
10.3760/cma.j.issn.0254-1424.2020.06.014
- VernacularTitle:肌肉再分布技术在智能仿生假肢信号识别中的应用
- Author:
Yong YANG
1
;
Qining WANG
;
Dongfang XU
;
Jingeng MAI
;
Liying SUN
;
Shanlin CHEN
;
Wen TIAN
;
Guanglei TIAN
Author Information
1. 北京积水潭医院手外科，北京 100035
- From:
Chinese Journal of Physical Medicine and Rehabilitation
2020;42(6):541-545
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the utility of the muscle redistribution technique (MRT) in the signal recognition of an intelligent bionic prosthesis.Methods:Between December 2016 and April 2017, 4 male patients were treated with muscle redistribution procedures. Among them, 3 were upper limb amputees of the distal 1/3 of the forearm, at the carpometacarpal joint and at the midcarpal joint. One was a lower limb amputee at the distal 1/3 of the lower leg. In each case, 4-6 muscles and tendons in the stump were transferred and the tendons were anchored in different areas of the skin. When the muscle contracted actively, the tendon pulled the skin, resulting in obvious deformation of the skin in different areas. The skin′s deformation, capacitance signal data and postoperative complications were used as indicators in the evaluations. To measure the capacitance signals the patients were asked to grip, flex and extend the wrist, and flex and extend the fingers , or dorsi- and plantar-flex the ankle, and flex and extend the toes. With the help of capacitance sensors the limb′s deformation was analyzed.Results:Three months after the surgery the patients were able to actively control contraction of the transferred muscle and produce skin deformation. At the final follow-up, the effective deformation rate was 80% (16/20). Two kinds of classifiers were identified by linear discriminant analysis and quadratic discriminant analysis. In the upper limb, the overall recognition accuracies were 97.27% and 100% respectively, and the recognition accuracy of each action was 100%. In the lower limb, the overall recognition accuracies were 95.32% and 100%, and the recognition accuracy of each action was again 100%. In one case wound healing was delayed and several dressing changes were required.Conclusions:MRT can effectively output motion intentions and increase the number and intensity of motion signals. The procedure provides a novel way for better control of intelligent bionic prostheses.
