Automatic detection of spasticity from flexible wearable sensors.
UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing Maui Hawaii September, 2017(2017)
摘要
Spasticity is a condition that impairs voluntary muscle movements and physically debilitates persons across several neurological disorders, including stroke, multiple sclerosis and cerebral palsy. Assessing the progression of spasticity during clinical interventions and at home is key to rehabilitation efficacy and care management. Here we present electromyography (EMG) and motion data using skin-mounted, flexible and wireless sensors in a cohort of 13 individuals with stroke. We compute a set of 15 features from the EMG data and use machine learning to infer whether spasticity is present during movements of the knee and ankle joints. Using a Linear Discriminant Analysis (LDA) classifier, we show that our approach successfully discriminates voluntary contractions from spastic muscle contractions (AUC=0.94). These results show that continuous and non-invasive monitoring of spasticity symptoms could be applied to optimize and personalize rehabilitation regimens.
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关键词
Flexible electronics, Rehabilitation, Machine learning
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