Document Type

Article

Publication Date

2023

DOI

10.1216/jie.2023.35.355

Publication Title

Journal of Integral Equations and Applications

Volume

35

Issue

3

Pages

355-374

Abstract

Electromyogram (EMG) signals play a significant role in decoding muscle contraction information for robotic hand prosthesis controllers. Widely applied decoders require a large amount of EMG signals sensors, resulting in complicated calculations and unsatisfactory predictions. By the biomechanical process of single degree-of-freedom human hand movements, only several EMG signals are essential for accurate predictions. Recently, a novel predictor of hand movements adopted a multistage sequential adaptive functional estimation (SAFE) method based on the historical functional linear model (FLM) to select important EMG signals and provide precise projections.

However, SAFE repeatedly performs matrix-vector multiplications with a dense representation matrix of the integral operator for the FLM, which is computationally expensive. Noting that with a properly chosen basis, the representation of the integral operator concentrates on a few bands of the basis, the goal of this study is to develop a fast multiscale SAFE (MSAFE) method aiming at reducing computational costs while preserving (or even improving) the accuracy of the original SAFE method. Specifically, a multiscale piecewise polynomial basis is adopted to discretize the integral operator for the FLM, resulting in an approximately sparse representation matrix, and then the matrix is truncated to a sparse one. This approach not only accelerates computations but also improves robustness against noises. When applied to real hand movement data, MSAFE saves 85%similar to 90% computing time compared with SAFE, while producing better sensor selection and comparable accuracy. In a simulation study, MSAFE shows stronger stability in sensor selection and prediction accuracy against correlated noise than SAFE.

Rights

© 2023 Rocky Mountain Mathematics Consortium. All rights reserved.

Included with the kind written permission of the copyright holder.

Original Publication Citation

Ren, J., Song, G., Tabacu, L., & Xu, Y. (2023). Fast multiscale functional estimation in optimal EMG placement for robotic prosthesis controllers. Journal of Integral Equations and Applications, 35(3), 355-374. https://doi.org/10.1216/jie.2023.35.355

ORCID

0000-0001-9930-5215 (Ren)

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