Development of a neural network based control algorithm for powered ankle prosthesis

Lower limb amputation is partial or complete removal of the limb due to disease, accident or trauma. Surface electromyograms (sEMG) of a large number of muscles and force sensors have been used to develop control algorithms for lower limb powered prostheses, but there are no commercial sEMG controlled prostheses available to date. Unlike ankle disarticulation, transtibial amputation yields less intact lower leg muscle mass. Therefore, minimizing the use of sEMG muscle sources utilized will make powered prosthesis controller economic, and limiting the use of specifically the lower leg muscles will make it flexible. Presently, we have used healthy population data to (1) test the feasibility of the neural network (NN) approach for developing a powered ankle prosthesis control algorithm that successfully predicts sagittal ankle angle and moment during walking using exclusively sEMG, and (2) rank all muscle combination variations according to their success to determine the economic and flexible NN's. sEMG amplitudes of five lower extremity muscles were used as inputs: the tibialis anterior (TA), medial gastrocnemius (MG), rectus femoris (RF), biceps femoris (BF) and gluteus maximus (GM). A time-delay feed-forward-multilayer-architecture NN algorithm was developed. Muscle combination variations were ranked using Pearson's correlation coefficient (r > 0.95 indicates successful correlations) and root-mean-square error between actual vs. estimated ankle position and moment. The trained NN TA + MG was successful (r_position = 0.952, r_moment = 0.997) whereas, TA + MG + BF (r_position = 0.981, r_moment = 0.996) and MG + BF + GM (r_position = 0.955, r_moment = 0.995) were distinguished as the economic and flexible variations, respectively. The algorithms developed should be trained and tested for data acquired from amputees in new studies.