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%)

Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques / Cesarini, Valerio; Costantini, Giovanni; Amato, Federica; Errico, Vito; Pietrosanti, Luca; Calado, Alexandre Luis; Massa, Roberto; Frezza, Erica; Irrera, Fernanda; Manoni, Alessandro; Saggio, Giovanni. - (2023), pp. 194-199. (Intervento presentato al convegno Metrology for Industry 4.0 and IoT 2023 tenutosi a Brescia (IT) nel 06-08 June 2023) [10.1109/MetroInd4.0IoT57462.2023.10180176].

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

Amato, Federica;
2023

Abstract

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%)
2023
979-8-3503-9657-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2981330