Selection of Machine Learning Algorithm for Pattern Recognition Based Bionic Devices: *Note: Sub-titles are not captured in Xplore and should not be used

The myoelectric prosthetic devices advancement has been essential to redevelop the grasping capabilities of amputees. Despite these advancements, myoelectric prosthetic devices need improvements to replicate the grasping performed by the human hand. The grasping performed by the human hand needs to be firm and avoid slippage of the objects. To avoid slippage, the information related to grasping force is important. In this study, the EMG signals are acquired while grasping a cylindrical object at different weight levels with two precision prismatic gestures. Using these EMG signals, force-based classification is performed based on the different weight levels for each gesture. The result shows that the highest mean classification accuracy was obtained using Support Vector Machines (SVM), followed by the k-Nearest Neighbors (k-NN) for each gesture. The mean classification accuracy obtained using SVM were 94.05% and 96.8% for 1st and 2nd gesture respectively. It is concluded that better outcomes are obtained using more complex classifiers as compared to simple classifiers such as Naïve Bayes and Linear Discriminant Analysis. In the future, a more descriptive and detailed analysis is expected to be performed using the outcomes obtained.