Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke

After stroke, many individuals develop impairments that lead to compensatory motions. Compensation allows individuals to achieve tasks but has long-term detrimental effects and represents maladaptive motor strategies. Increased use of bimanual motions may serve as a biomarker for recovery (and the reduction of reliance on compensatory motion), and tracking such motion using sensor data may provide critical data for health care specialists. However, past work by the authors demonstrated individual variation in motor strategies results in noisy and chaotic sensor data. The goal of the current work is to develop classifiers capable of differentiating unimanual, bimanaual asymmetric, and bimanual symmetric gestures using wearable sensor data. Twenty participants post-stroke (and 20 age-matched controls) performed a set of tasks under the supervision of a trained occupational therapist. Sensor data were recorded for each task. Classifiers were developed using artificial neural networks (ANNs) as a baseline, and the echo state neural network (ESNN) which has demonstrated efficacy with chaotic data. We find that, for control and post-stroke participants, the ESNN results in improved testing accuracy performance (91.3% and 80.3%, respectively). These results suggest a novel method for classifying gestures in individuals post-stroke, and the developed classifiers may facilitate longitudinal monitoring and correction of compensatory motion.