FEASIBILITY STUDY FOR ANFIS AND EMG UTILIZATION IN MODELING PROSTHESIS FOR TRANS-FEMORAL CUT REHABILITATION AND GAIT CYCLE RESTORATION

Abstract

Rehabilitation of lower limbs with high-level amputation (Trans-femoral Cut) requires an alternative method of inverse and forward dynamics to be employed. Inverse and forward dynamics are capable of solving the problem of redundancy of muscles in human lower limbs. Furthermore, inverse and forward dynamics can be used for estimating joints moments, predicting body segment kinematics, and estimating the distribution of forces between the agonist/antagonist muscle group with reasonable accuracy [Riener R, Lünenburger L, Colombo G, Human-Centered robotics applied to gait training and assessment robert, J Rehabil Res Dev43:679, 2006; Keller T, Perry JC, Rehabilitation robotics for outpatient clinical and domestic use, WC IFMBE Proc25:291, 2009; Crespo LM, Reinkensmeyer DJ, Review of control strategies for robotic movement training after neurologic injury, J Neuroeng Rehabil6:20, 2009; Hillman MI, Rehabilitation robotics from past to present — A historical prespective, Adv Rehabil Robotics, Lect Notes Contr Inf306:25, 2004.]. However, the application of these methods, in rehabilitation, is still limited due to their dependence on multiple input data that cannot be measured using conventional measurement tools. This study is aimed at verifying and validating new models which are capable of predicting body segment kinematics accurately using features obtained from electromyography (EMG) and adaptive neuro fuzzy inference system (ANFIS). These features are used to model a set of (input\output) dynamic data. EMG signals obtained from full gait cycle trials of a subject are used to calculate joint moments. Kinematics of the body segments are obtained by applying Euler numerical integration. The relative percentage root mean square (RMS) error between predicted kinematics from ANFIS and measured values for hip, knee, and ankle were 2.23%, 0.98%, and 0.69%, respectively. However, the achieved minimal reasonable accuracy for minimum number of measured inputs depends on the characteristics of the gait parameters such as cyclic nature and delayed symmetric activities between right and left leg about mid sagittal plane. Results of the combined model show that higher prediction accuracy of the gait cycle is achieved and so rehabilitation of lower limbs is feasible.