Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 30, Issue -, Pages 2589-2599Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2022.3204781
Keywords
Feature extraction; Skin; Stroke (medical condition); Wireless sensor networks; Wireless communication; Power harmonic filters; Low-pass filters; Stroke; upper-limb Brunnstrom classification; activities of daily living; pattern recognition; sensor reduction
Categories
Funding
- National Natural Science Foundation of China [62173094]
- Shanghai Municipal Science and Technology International Research and Development Collaboration Project [20510710500]
- Natural Science Foundation of Shanghai [20ZR1403400]
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Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective and rely heavily on therapist experience. This study explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) through three typical activities of daily living. By optimizing parameters and configurations, the proposed approach achieved reliable assessment using a small number of sensor modules, showing potential for home use.
Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.
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