Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds.
Qiang Zhang, Natalie Fragnito, Jason R Franz and 1 others
PMID 35945600WHAT IT FOUND
Combining muscle electrical signals with ultrasound thickness data improved the prediction of ankle push-off force during walking.
This method was more accurate than using either signal alone and remained robust across different walking speeds in healthy adults.
Key findings
01The model using both electromyography and ultrasound data significantly reduced prediction error compared to models using only one of these inputs.
02Calibrating the model with data from multiple walking speeds made it more robust across different speed scenarios than calibrating it at a single speed.
03The study was conducted on young participants without neuromuscular or orthopedic disorders.
STILL TO COME
How it was doneWhat they found
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What it does not show
The study included only ten healthy young adults, so the results may not apply to patients with walking impairments or neurological conditions. The ultrasound probe only measured two of the four main calf muscles, potentially missing contributions from other muscles. The tracking of muscle thickness via ultrasound is sensitive to the exact placement of the probe, which can introduce error. The study used a treadmill, which may not fully replicate the variability of overground walking.
Declared interests
The work was supported by the National Science Foundation.
The easy way to misread this
Do not assume this prediction model is ready for clinical use or that it works for patients with mobility impairments. The study tested only healthy young adults on a treadmill, and the authors state that further experiments on patients are needed to verify if the method is practical for them.