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Article

A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis

1
The BioRobotics Institute, Scuola Superiore Sant’Anna, viale Rinaldo Piaggio 34, Pontedera, 56025 Pisa, Italy
2
Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà, 33, 56127 Pisa, Italy
3
O.U. Neurology, Ospedale delle Apuane, AUSL Toscana Nord Ovest, via Enrico Mattei, 21, 54100 Massa, Italy
4
Department of Industrial Engineering, University of Florence, via Santa Marta 3, 50139 Florence, Italy
*
Author to whom correspondence should be addressed.
Received: 30 March 2020 / Revised: 29 April 2020 / Accepted: 3 May 2020 / Published: 5 May 2020
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring. View Full-Text
Keywords: decision support system; motion analysis; motor assessment; Parkinson’s disease diagnosis; signal processing; supervised learning; wearable inertial devices decision support system; motion analysis; motor assessment; Parkinson’s disease diagnosis; signal processing; supervised learning; wearable inertial devices
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MDPI and ACS Style

Rovini, E.; Maremmani, C.; Cavallo, F. A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis. Sensors 2020, 20, 2630. https://0-doi-org.brum.beds.ac.uk/10.3390/s20092630

AMA Style

Rovini E, Maremmani C, Cavallo F. A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis. Sensors. 2020; 20(9):2630. https://0-doi-org.brum.beds.ac.uk/10.3390/s20092630

Chicago/Turabian Style

Rovini, Erika, Carlo Maremmani, and Filippo Cavallo. 2020. "A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis" Sensors 20, no. 9: 2630. https://0-doi-org.brum.beds.ac.uk/10.3390/s20092630

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