Motor point identification is pivotal to elicit comfortable and sustained muscle contraction through functional electrical stimulation. To this purpose, anatomical charts and manual search techniques are used to extract subject-specific stimulation profile. Such information being heterogenous they lack standardization and reproducibility. To address these limitations; we aim to identify, localize, and characterize the motor points of forearm muscles across nine healthy subjects.
Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. Inspired by the phenomenon of atomic fission, this paper proposes a novel density-based clustering algorithm, called fission clustering (FC). It focuses on mining the dense families of clusters in the dataset and utilizes the information of the distance matrix to fissure the dataset into subsets.