Implementation of an Assortment of Machine Learning Classification Algorithms Regarding Diadochokinesia for Hemiparesis with Quantification from Conformal Wearable and Wireless System

Abstract

Diadochokinesia pertains to a standard aspect of the conventional neurological examination, which involves the oscillation between muscle groups with an agonist and antagonist relationship. A representative example is the pronation and supination of the forearm. Hemiparesis visibly demonstrates disparity of diadochokinesia, and clinical quantification is achieved through the use of an ordinal scale, which is inherently subjective. A conformal wearable and wireless inertial sensor equipped with a gyroscope mounted about the dorsum of the hand can objectively quantify diadochokinesia respective of forearm pronation and supination. The objective of the research endeavor was to apply an assortment of machine learning algorithms to distinguish between a hemiplegic affected and unaffected upper limb pair based on diadochokinesia with respect to pronation and supination of the forearm. Performance of the machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in consideration of classification accuracy and time to develop the machine learning model. The machine learning feature set was derived from the acquired gyroscope signal data. Using the gyroscope signal data from the conformal wearable and wireless inertial sensor the logistic regression and naïve Bayes machine learning algorithms achieved considerable performance capability with respect to both time to converge the machine learning model and classification accuracy for distinguishing between a hemiplegic upper limb pair for diadochokinesia in consideration of pronation and supination.