ArticleJournal of neuroengineering and rehabilitation2025
Sonomyography accurately captures joint kinematics during volitional and electrically stimulated motion in healthy adults and an individual with cerebral palsy.
Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Invariant properties of upper limb movement trajectory control.Journal of neural engineering · 2026Article
- Article
- Assessing the Resilience of sEMG Classifiers to Sensor Malfunction and Signal Saturation.Sensors (Basel, Switzerland) · 2026Article
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5 authors.
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Abstract
backgroundDespite significant advances in biosignal extraction techniques for studying neuromotor disorders, there remains an unmet need for a method that effectively links muscle structure and dynamics to muscle activation. Addressing this gap could improve the quantification of neuromuscular impairments and pave the way for precision rehabilitation. In this study, we demonstrate the proof of concept of recording multimodal signals from the brain, muscles, and resulting limb kinematics. We also explore the use of ultrasound imaging to extract limb kinematics.
methodsWe collected data from three healthy volunteers and one individual with cerebral palsy during single degree-of-freedom ankle and wrist movements. Participants performed range of motion (ROM) tasks at approximately 1-second intervals, either volitionally or through functional electrical stimulation. We simultaneously recorded electroencephalography, surface electromyography (EMG), continuous ultrasound imaging, and motion capture data. Joint kinematics were computed from ultrasound imaging using a technique called sonomyography (SMG), and we evaluated the technical feasibility of estimating joint kinematics from both sonomyography and surface EMG signals.
resultsThe technical feasibility study evaluated joint angle prediction using EMG and SMG under volitional (FES-OFF) and electrically stimulated (FES-ON) conditions. Root mean squared error (RMSE) between predicted and measured joint angles was computed for multiple methods of extracting kinematics from EMG and SMG. EMG-based RMSE ranged from 0.34 to 0.57 (FES-OFF) and 0.43-0.51 (FES-ON). SMG-based RMSE ranged from 0.10 to 0.25 across all conditions and methods. Linear regression analysis produced
conclusionOur findings suggest that sonomyography can be used as a noninvasive method for estimating joint kinematics when the joint movement is driven either by volition or by functional electrical stimulation. This technique can potentially be be useful in evaluating altered muscle dynamics and driving assistive and rehabilitation devices in individuals with neuromotor disorders such as cerebral palsy.
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