Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques

this paper deals with the automatic detection of myotonia from a task based on the sudden opening of the hand. data have been gathered from 44 subjects, divided into 17 controls and 27 myotonic patients, by measuring a 2-point articulation of each finger thanks to a calibrated sensory glove equipped with a resistive flex sensor (RFS). RFS gloves are proven to be reliable in the analysis of motion for myotonic patients, which is a relevant task for the monitoring of the disease and subsequent treatment. with the focus on a healthy VS pathological comparison, customized features were extracted, and several classifications entailing motion data from single fingers, single articulations and aggregations were prepared. the pipeline employed a correlation-based feature selector followed by a SVM classifier. results prove that it's possible to detect myotonia, with aggregated data from four fingers and upper/lower articulations providing the most promising accuracies (91.1%).