Sparse Sonomyography-Based Estimation of Isometric Force: A Comparison of Methods and Features

Noninvasive methods for estimation of joint and muscle forces have widespread clinical and research applications. Surface electromyography (sEMG) provides a measure of the neural activation of muscles which can be used to estimate the force produced by the muscle. However, sEMG based measures of for...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical robotics and bionics 2022-08, Vol.4 (3), p.821-829
Main Authors: Kamatham, Anne Tryphosa, Alzamani, Meena, Dockum, Allison, Sikdar, Siddhartha, Mukherjee, Biswarup
Format: Article
Language:English
Subjects:
A-mode ultrasound
Electromyography
Estimation
Feature extraction
Force
force estimation
Gaussian process
muscle activity sensing
Muscles
Performance evaluation
Regression models
Signal to noise ratio
sonomyography
Transducers
Ultrasonic imaging
Ultrasonic testing
Ultrasonic variables measurement
Wearable technology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Noninvasive methods for estimation of joint and muscle forces have widespread clinical and research applications. Surface electromyography (sEMG) provides a measure of the neural activation of muscles which can be used to estimate the force produced by the muscle. However, sEMG based measures of force suffer from poor signal-to-noise ratio and limited spatiotemporal specificity. In this paper, we propose a sonomyography or ultrasound imaging-based offline approach for estimating continuous isometric force from a sparse set of ultrasound scanlines. Our approach isolates anatomically relevant features from A-mode scanlines isolated from B-mode images, thus greatly reducing the dimensionality of the feature space and the computational complexity involved in traditional ultrasound-based methods. We evaluate the performance of four regression methodologies for force prediction using the reduced sonomyographic feature set. We also evaluate the feasibility of a practical wearable sonomyography-based system by simulating the effect of transducer placement and varying the number of transducers used in force prediction. Our results demonstrate that Gaussian process regression models outperform other regression methods in predicting continuous force levels from just four equispaced transducers in offline settings. We believe that these findings will aid in the design of wearable, robust and computationally efficient sonomyography-based force prediction systems.
ISSN:2576-3202
2576-3202
DOI:10.1109/TMRB.2022.3172680