Elbow spasticity during passive stretch-reflex: clinical evaluation using a wearable sensor system
Bibliographic record
Abstract
BACKGROUND: Spasticity is a prevalent chronic condition among persons with upper motor neuron syndrome that significantly impacts function and can be costly to treat. Clinical assessment is most often performed with passive stretch-reflex tests and graded on a scale, such as the Modified Ashworth Scale (MAS). However, these scales are limited in sensitivity and are highly subjective. This paper shows that a simple wearable sensor system (angle sensor and 2-channel EMG) worn during a stretch-reflex assessment can be used to more objectively quantify spasticity in a clinical setting. METHODS: A wearable sensor system consisting of a fibre-optic goniometer and 2-channel electromyography (EMG) was used to capture data during administration of the passive stretch-reflex test for elbow flexor and extensor spasticity. A kinematic model of unrestricted passive joint motion was used to extract metrics from the kinematic and EMG data to represent the intensity of the involuntary reflex. Relationships between the biometric results and clinical measures (MAS, isometric muscle strength and passive range of motion) were explored. RESULTS: Preliminary results based on nine patients with varying degrees of flexor and extensor spasticity showed that kinematic and EMG derived metrics were strongly correlated with one another, were correlated positively (and significantly) with clinical MAS, and negatively correlated (though mostly non-significant) with isometric muscle strength. CONCLUSIONS: We conclude that a wearable sensor system used in conjunction with a simple kinematic model can capture clinically relevant features of elbow spasticity during stretch-reflex testing in a clinical environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".