A Real Time Surface Electromyography Signal Driven Prosthetic Hand Model Using PID Controlled DC Motor

• Published in: Biomedical engineering letters pp. 276 - 286
• Main Authors: Retheep Raj, Rejith Ramakrishnan, Karingannikalam Subramanian Sivanandan
• Format: Journal Article
• Language: English
• Published: 대한의용생체공학회 01.11.2016
• Subjects: 의공학
• ISSN: 2093-9868; 2093-985X
• DOI: 10.1007/s13534-016-0225-3

Purpose A surface electromyography (sEMG) drivenproportional-integral-derivative (PID) control method isproposed to control the prosthetic hand model according tohuman intentions in real time. Methods The sEMG signals are acquired from the bicepsand triceps brachii muscles of the human hand from 30 ablebodied subjects. Two time domain features, integrated EMG(IEMG) and number of zero crossing (ZC) are extractedfrom the sEMG signals and these features are used for theestimation of human forearm kinematics. The estimation ofhuman forearm kinematics is achieved by multi layeredperceptron neural network (MLPNN) model based on nonlinearauto regressive with exogenous (NARX) inputs. Theestimated human kinematics are utilized to control a directcurrent (DC) motor based prosthetic hand model using PIDcontroller. The controller parameters are tuned manually toobtain the best possible results. Results It is observed that the IEMG and ZC varies withchange in angular displacement and also with change inangular velocity. The performance of estimation and controlis evaluated using two statistical parameters, root meansquare error (RMSE) and regression value. The RMSE andregression value obtained during estimation of angulardisplacement is 5.89 and 0.97 and the corresponding valueobtained for the estimation of angular velocity is 18.91 and0.80. The RMSE and regression value obtained duringcontrol of angular displacement is 18.9096 and 0.9456 andthe corresponding value obtained for the control of angularvelocity is 27.91 and 0.68. Conclusions Experimental results confirm that the estimationusing MLPNN and PID controlled prosthetic DC motor handmodel performs well. The proposed method is simple indesign and can be implemented in a human being with fewer costs. KCI Citation Count: 0