Study of thumb attitude relationship to extrinsic muscles characterization

Abstract

In the case of amputees, the development of cybernetic hands that closely resemble the functions of real hands is essential for comfort and functionality purposes. Controlled by intrinsic and extrinsic muscles, the human thumb plays a major role in differentiating hand gestures. For those who have lost their intrinsic hand muscles, any information about muscle activities that can be obtained from the extrinsic muscles is essential to control the thumb. Thus, focusing on transradial amputees, this research investigates the relationship between extrinsic muscles to characterise thumb posture. A High-Density surface Electromyogram (HD-sEMG) device and a portable thumb force measurement system were used to collect forearm HD-sEMG signals from a total of 17 subjects. For the flexion motion, the subjects were asked to repetitively place their thumb at rest before exerting 30% of their individual maximum voluntary contraction (MVC) on a load cell by following a designated trajectory presented on a developed graphical user interface (GUI). The measurement system was set to four different postures namely zero degrees, thirty degrees, sixty degrees, and ninety degrees. Feature extraction was then performed by extracting the absolute rectified value (ARV), root mean square (RMS), mean frequency (MNF) and median frequency (MDF) values of the forearm HD-sEMG signals before being classified using four different classifiers namely linear discriminant analysis (LDA), support vector machine (SVM), k-Nearest Neighbour (KNN), and TREE-based classifier. The results revealed that the LDA classified RMS and ARV-RMS features, which were extracted from both posterior and anterior hand sides successfully achieved the highest correctly classified percentage of 99.7%. The findings of the study are significant for the development of a dedicated model-based control framework for prosthesis hand development to be used by transradial amputees in the near future.