Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains

Abstract

Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten ablebodied individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.

Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression

“…This gives further justification to the increased resolution, as well as ability to access deep muscle tissue, as probable explanations for the improved regression performance of sonomyography in comparison to surface EMG. These results are in agreement with our previous work demonstrating increased performance of sonomyography in comparison to surface EMG for ambulation mode classification, as well as high performance of sonomyography-based knee angular velocity prediction and hip, knee and ankle joint moment prediction ( Rabe et al, 2020a ; Rabe et al, 2021a ; Rabe et al, 2021b ).…”

“…Many researchers have evaluated these ultrasound-based features of muscle morphology for correlations with muscle force production, muscle contraction, and joint motion as well as overall muscle strength and muscle fatigue ( Kurokawa et al, 2001 ; Muraoka et al, 2005 ; Blazevich et al, 2006 ; Han et al, 2013 ; Panizzolo et al, 2013 ; Li et al, 2020 ). Based on previous research demonstrating these features are useful for estimation of knee kinematics from sonomyography, mean intensity and temporal intensity features were extracted from each ultrasound imaging frame, as described in detail previously ( Jahanandish et al, 2019a ; Jahanandish et al, 2019b ; Rabe et al, 2020a ; Rabe et al, 2021a ; Rabe et al, 2021b ). The image sequence from each trial was split by heel strikes to create an ultrasound image sequence for each stride.…”

Estimating human joint moments unifies exoskeleton control, reducing user effort

“…Instead, instantaneous estimates of the user's underlying joint moments could replace conventional high-level states (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39). Because lower-limb joint moments naturally vary across ambulation modes and conditions (40), lower-limb joint moments could serve as a single, continuous high-level state for modulating exoskeleton assistance.…”

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks

“…B-mode ultrasound generates a 2D sonomyography image revealing the underlying muscle bellies. In lower limb studies with ablebodied subjects, changes in these ultrasound images have been used for the continuous classification of ambulation modes [35], and the estimation of lower-limb kinematics [37][38] and kinetics [39]. B-mode ultrasound-based measurements of muscle fatigue [40] and muscle force [41] have also been incorporated into the control of lower-limb hybrid exoskeletons [42].…”