Design & Development of 3 Degrees of Freedom (DoF) Real-Time Game for Post-Stroke Rehabilitation Using Fitts’ Law & Pattern Recognition /
Urwah Imran
- 98p. Soft Copy 30cm
This research addresses challenges in stroke rehabilitation by proposing an alternative to current methods that are often costly and resource-intensive. With limited accessibility to rehabilitation interventions due to constraints in skilled therapists and advanced technology, there is a need for innovative solutions. Past research predominantly focused on 2-DoF systems, limiting exploration in diverse multiple DoF EMG classification systems. The initial phase of this study involves a comprehensive investigation into the potential of user-specific non-functional hand movements. By examining 16 distinct wrist and finger motions using five classifiers (Wide Neural Networks, Ensemble Bagged Trees, Cubic SVM, Fine KNN, Logistic Regression Kernel), significant differences were revealed. One-Way Anova testing (p<0.05) revealed significant classifier differences while Wilicoxon signed-rank tests (p<0.05) distinguished individual finger and wrist getsures. For the development of a 3-DOF real-time game, specific hand movements were strategically selected based on the best obtained accuracies. Noteworthy movements include "Abduction of thumb and flexion of others," "Flexion of all fingers," "Extension of index and flexion of others”, “Wrist Radial Deviation (Clockwise)”, Wrist Extension”, and “Wrist Supination Axis: middle finger.” A MATLAB APP DESIGNER-based GUI was developed with two tabs. Real-time metrics for the designed game exhibited promising results: a completion rate of 94.22% (±2.74%), throughput of 0.40 bits s−1 (±0.03), path efficiency of 87.02%, and minimal overshoot of 12.68%. These findings underscore the game's potential as an effective and engaging tool for stroke rehabilitation, offering a novel approach to address the challenges associated with current methods.