Evaluation of misclassification matrix method in validation of an assistive device for manual wheelchair propulsion

Classification accuracy is essential in the bio signal’s performance-based assistive devices. In this study, surface electromyography (SEMG) signals acquisition was extracted from 3 healthy right-handed participants. SEMG signal was processed, and Motor Unit Action Potential (MUAP) was determined. Accuracy, precision, sensitivity and specificity were calculated in real-time based on individual MUAP, critically compared with pattern and non-pattern recognition control methods by Misclassification Matrix inserted into Arduino MEGA 2560 Microcontroller. The results indicated that the performance of each control method is different for every participant and a comparison tool is a must to select the best out of it. It shows that the misclassification matrix filtered the best control method for participant 1 as Probability Density Function, no for participant 2 and Maximum Point Different (MPD) for participant 3 based on determined conditions