Fast Multiscale Functional Estimation in Optimal EMG Placement for Robotic Prosthesis Controllers

TL;DR

This paper introduces a fast multiscale functional estimation method for EMG-based prosthesis control, significantly reducing computation time while maintaining or improving prediction accuracy and robustness.

Contribution

It develops a multiscale SAFE method using a sparse basis to accelerate computations and enhance noise robustness in EMG signal-based hand movement prediction.

Findings

MSAFE reduces computation time by 85-90% compared to SAFE.

MSAFE improves sensor selection stability and noise robustness.

MSAFE achieves comparable or better prediction accuracy.

Abstract

Electrocardiogram (EMG) signals play a significant role in decoding muscle contraction information for robotic hand prosthesis controllers. Widely applied decoders require 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 adopts a multistage Sequential, Adaptive Functional Estimation (SAFE) method based on 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 computational expansive. 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%$\sim$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.