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The performance of activities of daily living (ADLs) is directly related to recovery of motor function after stroke. Because the recovery process occurs primarily in the home, there is a need for tools sensitive to this process that can be used in ambient settings. The goal of the current approach is to use surface electromyography (sEMG) acquired from wearable sensors to capture relevant ADL performance. Our specific focus is on detecting thumb-forefinger aperture. This aperture, which occurs during reach-to-grasp (RTG) movements, is an indicator of potential success of interacting with the environment. Our results suggest that sEMG data can be used to determine increasing thumb-forefinger aperture in a population of non-disabled individuals. We find a statistically significant effect of increased aperture on peak sEMG values (p < 0.001). 

Abstract Background Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited. Objective To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance. Methods Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0–1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM). Results A significant group × task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality. Conclusions Our algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians. 

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.