Use of Sonomyographic Sensing to Estimate Knee Angular Velocity During Varying Modes of Ambulation

Rabe, Kaitlin G.; Jahanandish, Mohammad Hassan; Hoyt, Kenneth; Fey, Nicholas P.

doi:10.1109/embc44109.2020.9176674

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…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 ).…”

Section: Discussionsupporting

confidence: 93%

“…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.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, previous research demonstrated improvement to classification of discrete ambulation tasks by sonomyography compared to surface electromyography ( Rabe et al, 2021a ). Further, sonomyography of the lower limb has been used to continuously estimate knee kinematics ( Jahanandish et al, 2019b ; Rabe et al, 2020a ), as well as hip, knee and ankle moments during basic walking tasks and isometric contraction ( Rabe et al, 2020b ; Zhang et al, 2020 ; Zhang et al, 2021 ). However, the ability of sonomyography to continuously predict multiple joint kinematics during widely varying ambulation tasks encompassing level ground, ramp incline, ramp decline, stair ascent and stair descent and how the performance compares to surface EMG as well as the fusion of surface EMG with sonomyography is unknown.…”

Section: Introductionmentioning

confidence: 99%

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

Rabe

Fey

2022

Front. Robot. AI

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Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.

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Section: Discussionsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rabe

Fey

2022

Front. Robot. AI

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“…In the lower limb, researchers have evaluated the contribution of ultrasound features from muscles spanning the ankle for estimation of ankle joint kinetics [28]. Furthermore, we have previously demonstrated high accuracy of sonomyography of the quadriceps muscles for continuous classification of ambulation mode [23] as well as continuous estimation of lower-limb joint kinematics and kinetics in ablebodied subjects [29], [30]. However, there have been no known comparisons of surface EMG to sonomyography for continuous estimation of multiple lower-limb joint torques during ambulation tasks of differing biomechanical demand.…”

Section: Introductionmentioning

confidence: 99%

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

Rabe¹,

Lenzi

Fey

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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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.

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Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion

et al. 2020

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Use of Sonomyographic Sensing to Estimate Knee Angular Velocity During Varying Modes of Ambulation

Cited by 9 publications

References 17 publications

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

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

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

Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion

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