Date of Award
Master of Science in Mechanical Engineering (MS)
College, School or Department Name
Department of Mechanical Engineering-Engineering Mechanics
This paper reports on the feasibility of developing a model to describe the nonlinear relationship between the mechanical impedance of the human ankle within a specified range of frequency and the root mean square (RMS) value of the Electromyography (EMG) signals of the muscles of human ankle using Artificial Neural Network (ANN). A lower extremity rehabilitation robot — Anklebot was used to apply pseudo-random mechanical perturbations to the ankle and measure the angular displacement of the ankle to estimate the data of ankle mechanical impedance. Meanwhile, the surface EMG signals from the selected muscles were monitored and recorded using a Delsys Trigno® system. The final ANN models in this paper were created in two degrees of freedom — dorsiflexion-plantarflexion (DP) and inversioneversion (IE) at 3 different muscle activation levels. The results of analysis of the ANN model showed the feasibility of developing models with adequate accuracy and to define the mechanical impedance of the human ankle in terms of lower extremity muscles’ EMG statistical properties.
JIA, Chen, "MECHANICAL IMPEDANCE OF ANKLE AS A FUNCTION OF ELECTROMYOGRAPHY SIGNALS OF LOWER LEG MUSCLES USING ARTIFICIAL NEURAL NETWORK", Master's Thesis, Michigan Technological University, 2015.