Estimating output torque via amplitude estimation and neural drive : a high-density sEMG study
Author(s)Leahy, Logan Patrick.
High-density surface electromyography study
Massachusetts Institute of Technology. Department of Mechanical Engineering.
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The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. One such application is the augmentation of healthy individuals for improved performance. A challenge within this field is improving user-interface control. An established approach for improving user-interface control is neural control interfaces derived from surface electromyography (sEMG). This thesis presents an exploration of output joint torque estimation using high density surface electromyography (HDsEMG). The specific aims of this thesis were to implement a well-established amplitude estimation method for standard multi-electrode sEMG collection with an HDsEMG grid, and to take an existing blind source separation algorithm for HDsEMG decomposition and modify it in order to decompose a nonisometric contraction.In order to meet our study objectives, a novel dataset of simultaneous HDsEMG collected from the tibialis anterior muscle and torque output measures during controlled ankle movements was acquired. This data collection was conducted at The Army Research Laboratory. Data was collected for six subjects across three test conditions. The three test conditions were an isometric ramp-and-hold contraction, a force-varying isometric sinusoidal contraction, and a dynamic isotonic contraction. The amplitude estimation method used has been well-established but has not yet been explored for HDsEMG grids. In the exploration, three factors were varied: the number of channels on the grid used, the spatial area covered by the grid, and the signal whitening condition (no whitening, conventional whitening, and adaptive whitening).The findings were that (1) Reducing the number of channels used while covering a constant spatial area did not diminish the output torque estimate, (2) Reducing the spatial area covered for a constant number of channels did not diminish the output torque estimate, and (3) For higher levels of contraction, adaptive whitening performed worse than conventional whitening and no whitening. The results suggest adaptive whitening is not a suitable method for HDsEMG. These findings are encouraging for developing an improved signal for myoelectric control: smaller, less expensive grids that use computationally less taxing methods could be utilized to achieve comparable, if not better, results. A blind source separation method based on iterative deconvolution of HDsEMG using independent component analysis was implemented to identify individual motor unit spike trains. Two methods were then used to generate the neural drive profile: rate coding and kernel smoothing.A looped decomposition method was implemented for estimating output torque during the isotonic contraction. Even in the most controlled setting for a primarily single joint muscle, the modification of the algorithm did not represent the full population of the active motor units; thus, torque estimation was poor. There are still significant limitations in moving towards predicting output torque during dynamic contractions using this neural drive method. Although the decomposition of a non-isometric contraction was not successful, a contribution of this thesis work was identifying that the decomposition algorithm implemented may be biased towards larger motor units. This independently substantiated the same observation reported in a study published during the course of this thesis.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 115-121).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology