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This paper present the development of paraplegic quadriceps muscle model based on Functional Electrical Stimulation (FES). A type of modeling, Artificial Neural Network (ANN) were used to investigate the impact of different stimulation frequency, pulse width, pulse duration and settling time on the movement of quadriceps muscle with paraplegia due to a spinal cord injury. 361 training data and 300 testing data set are used in the development of muscle model. Two type of learning approach which is feed-forward backpropagation and cascade-forward backpropagation are considered to develop quadriceps muscle model. The developed model then, validated with clinical data. The model of muscle presented is able to accurately predict muscle torque outputs, along with the variability of the identified parameter. In this study, the feed-forward NN muscle model is found to be the most accurate muscle model representing paraplegic quadriceps muscle model. The established model is then used to predict the behaviour of the underlying system and will be used in the future for the design and evaluation of various control strategies

The Development of Quadriceps Muscle Model for Paraplegic