Characterization of the Stroke-Induced Changes in the Variability and Complexity of Handgrip Force

Poststroke Grasp Ability Assessment using an Intelligent Data Glove based on Action Research Arm Test: Development, Algorithms, and Experiments

Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.

Investigating the Stroke- and Aging-Related Changes in Global and Instantaneous Intermuscular Coupling Using Cross-Fuzzy Entropy

Intermuscular coupling is essential in the coordination of agonist and antagonist muscles. However, its dynamic characteristics are not fully understood, especially the alterations of intermuscular coupling induced by stroke and aging. This study aimed to investigate the aging- and stroke-related changes in the global and instantaneous intermuscular coupling between agonist and antagonist muscles. In the experiment, 8 patients after stroke, 18 healthy young subjects and 10 healthy middle-aged subjects were recruited and instructed to finish the elbow flexion and extension tasks. Cross-fuzzy entropy (C-FuzzyEn) and instantaneous C-FuzzyEn ( [Formula: see text]-FuzzyEn) based on a sliding window were used to analyze the global and instantaneous intermuscular coupling, respectively. Instantaneous FuzzyEn ( i -FuzzyEn) based on a sliding window was also applied to investigate the dynamic complexity of the EMG segment. Pearson correlation analysis revealed that i -FuzzyEn values were negatively correlated with [Formula: see text]-FuzzyEn values in most cases, which implied that there was a positive correlation between EMG complexity and intermuscular coupling. The C-FuzzyEn values between agonist and antagonist muscles increased significantly in both tasks of the patients after stroke than those of the healthy subjects (p < 0.05), which might be due to the decrease in intermuscular coupling induced by the damage of the corticospinal pathways after stroke. The combined application of C-FuzzyEn, [Formula: see text]-FuzzyEn and i -FuzzyEn provides a more comprehensive understanding of the global and instantaneous intermuscular coupling.