Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation | JoVE Visualize

Area of Science:

Biomechanics
Musculoskeletal dynamics
Assistive device control

Background:

Current methods for measuring in vivo muscle force in real-time are lacking, hindering biomechanical research and intuitive control of assistive technologies.
Surface electromyography (sEMG) is a common method for assessing muscle activation but has limitations in directly reflecting muscle output force.

Purpose of the Study:

To investigate muscle deformation as a novel, noninvasive signal for inferring real-time muscle output force.
To explore the utility of muscle deformation for developing intuitive control schemes for assistive devices.
To compare muscle deformation-based control with sEMG-based control.

Main Methods:

Utilized ultrasound and optical flow to measure brachioradialis muscle thickness changes during isometric elbow contractions in 10 subjects.
Correlated muscle deformation with elbow output force and compared it to sEMG activation.
Assessed subjects' ability to perform a trajectory tracking task using real-time visual feedback from the deformation signal.
Collected user preference data comparing deformation-based and sEMG-based control.

Main Results:

Muscle deformation (brachioradialis thickness change) showed a strong correlation with elbow output force, comparable to sEMG activation.
Subjects could effectively perform a trajectory tracking task using the muscle deformation signal.
The deformation-based control scheme was largely preferred by subjects over the sEMG-based scheme, with similar task accuracy.
The localized nature and direct mechanistic link of muscle deformation to force offer extensibility to multiple muscles and device degrees of freedom.

Conclusions:

Muscle deformation is a promising, unexplored signal for noninvasive, real-time measurement of muscle output force.
Muscle deformation-based control schemes are intuitive, preferred by users, and comparable in accuracy to sEMG-based methods.
This approach has significant potential for advancing biomechanical studies of muscle dynamics and enabling more sophisticated control of assistive devices.