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Activity Number: 481 - Nonparametric Methods in Functional Data Analysis
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Nonparametric Statistics
Abstract #312381
Title: New Methods for Optimal EMG Placement for a Robotic Prosthesis Controller
Author(s): Julia Holter* and Helen Huang and Arnab Maity and Ana Maria Staicu and Jonathan Stallrich
Companies: North Carolina State University and North Carolina State University and North Carolina State University and North Carolina State University and North Carolina State University
Keywords: Electromyography; Functional data; Functional linear model; Pattern Recognition; Kernel Machine
Abstract:

Robotic prostheses require a controller to decode muscle contractions, measured by electromyogram (EMG) signals, into the user’s desired hand movement. While EMG data collected across hundreds of sensors is informative, it is inefficient and impractical. Therefore, choosing the optimal locations for EMG sensor placement is an important problem, but there are few methods available to perform this selection. In this talk, we overview existing controllers based on pattern recognition of summarized EMG data, and argue that controllers that treat the EMG data as functional covariates are more appropriate. With this perspective, we introduce new methods based on linear and nonlinear functional additive models that are able to perform simultaneous EMG sensor selection and prosthesis-controller tuning. These methods are able to overcome challenges unique to high-density EMG data, like high cross-correlation, and are shown to outperform the prediction performance of existing methods.


Authors who are presenting talks have a * after their name.

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