Comparative study on estimation of elbow kinematics based on EMG time domain parameters using neural network and ANFIS NARX model

In this work, angular displacement and angular velocity of the elbow during continuous flexion and extension movement are estimated using three different models, with Surface Electromyography (SEMG) time domain parameters as model inputs, and the results are compared to select the model that gives the most accurate results. Surface Electromyography (SEMG) is recorded using surface electrodes placed on the biceps brachii muscles during continuous flexion and extension of the elbow joint. SEMG recording is done with elbow angle changing at different angular velocities. The obtained SEMG signals are segmented into 250-millisecond duration windows using disjoint windowing technique. Two time-domain parameters, Integrated Electromyography (IEMG) and Zero crossing (ZC) are derived from each windowed SEMG signals. The obtained value of IEMG and ZC are fed as the inputs to the Multiple Input Multiple Output (MIMO) model for the estimation of elbow angular displacement and elbow angular velocity. In this work three Multiple Input Multiple Output (MIMO) nonlinear black box model are developed using a Nonlinear Auto Regressive with eXogenous inputs (NARX) structure: (1) Multi-Layered Perceptron Neural Network (MLPNN) model based on NARX input, (2) Elman neural network model based on NARX input and (3) Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on NARX input. The results obtained from the three different models are compared using statistical parameters like regression coefficient and root mean square error (RSME). Based on this comparison the paper proposes that the estimation of elbow kinematics using ANFIS NARX model gives more accurate results when compared with Elman NARX model and Multi-Layered Perceptron Neural Network (MLPNN) NARX model.