Elbow spasticity during passive stretch-reflex: Clinical evaluation using a wearable sensor system

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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. © 2013 McGibbon et al.; licensee BioMed Central Ltd.

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CITATION STYLE

APA

McGibbon, C. A., Sexton, A., Jones, M., & O’Connell, C. (2013). Elbow spasticity during passive stretch-reflex: Clinical evaluation using a wearable sensor system. Journal of NeuroEngineering and Rehabilitation, 10(1). https://doi.org/10.1186/1743-0003-10-61

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