PCA and deep learning based myoelectric grasping control of a prosthetic hand

• Published in: Biomedical engineering online Vol. 17; no. 1; pp. 107 - 18
• Main Authors: Li, Chuanjiang, Ren, Jian, Huang, Huaiqi, Wang, Bin, Zhu, Yanfei, Hu, Huosheng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.08.2018; BioMed Central; BMC
• Subjects: Algorithms; Amputation; Arm; Artificial neural networks; Classification; Control methods; Control stability; Data processing; Deep learning; Discriminant analysis; DNN; Electromyography; Feature extraction; Force; Fuzzy control; Fuzzy controller; Grasp control; Grasping; Grasping force; Hand; Health aspects; Implants, Artificial; Muscle contraction; Myoelectric prosthesis; Myoelectricity; Neural networks; Pattern recognition; PCA; Predictive control; Principal components analysis; Process controls; Prostheses; Prosthesis; Prosthetic hand; Quality of life; Recognition; Reduction; Regression analysis; Regression models; Segmentation; sEMG-force; Statistical analysis; Vibration; DNN; Grasp control; sEMG-force; Fuzzy controller; Vibration feedback device; Prosthetic hand; PCA

For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of pro...