Estimation of Gait Parameters using EMG Signal with Extreme Learning Machine

Han-Leong Lim, Jee-Hou Ho, Kevin Lee


In this paper, an algorithm to estimate the gait parameters based upon EMG signal is proposed. The algorithm is developed using extreme learning machine (ELM). Experiments were conducted to acquire the gait parameters from 18 healthy human subjects. EMG signals from Tibialis Anterior (TA) and Gastrocnemius Lateral (GL) muscles were obtained during the gait cycle. The target temporal gait parameters are gait speed and stance/swing phase which were measured using inertia sensor and camera system. The ELM algorithm was developed using a single hidden layer feedforward network architecture where the weights from the input layer to the hidden layer are randomized and not updated during the run. Results obtained from ELM were compared with artificial neural network (ANN) model with the same architecture as the ELM algorithm. In ELM, the mean estimation errors of gait speed, stance percentage, and swing percentage were 11.86%, 7.62%, and 6.07% respectively. This was compared to the errors of 12.92%, 11.75% and 9.56% using ANN. Besides that, ELM achieved shorter training and testing time. The robustness of ELM algorithm demonstrated the capability of real-time computation due to superior computing performance compared to conventional ANN models.


Artificial Neural Network; Extreme Learning Machine; Gait Analysis;

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