Diadochokinesia Distinction of Hemiparesis through Quantification of Conformal Wearable and Wireless Inertial Sensor with Machine Learning

A standard aspect of the neurological examination involves the assessment of diadochokinesia. Diadochokinesia pertains the other swift alternation of an agonist and antagonist muscle pair, such as pronation and supination with respect to the forearm. For example, hemiparesis can manifest perceptible distinction for diadochokinesia with respect to the hemiplegic affected forearm and unaffected forearm. Standard techniques involve ordinal scales, which are inherently subjective, and the development of quantified methods has been recommended. The advent of conformal wearable and wireless inertial sensor systems, such as the BioStamp nPoint with a lightweight and bandage-like profile, enables quantification of diadochokinesia with wireless access to a Cloud computing environment for post-processing. The gyroscope signal data quantifying diadochokinesia can be consolidated to a feature set for machine learning classification to differentiate between a hemiplegic affected forearm and unaffected forearm. Considerable machine learning classification accuracy is attained for distinguishing between a hemiplegic affected forearm and unaffected forearm based on the quantified gyroscope signal data acquired from a conformal wearable and wireless inertial sensor system.