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Article

An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease

by
Giovanni Albani
1,
Claudia Ferraris
2,3,
Roberto Nerino
2,*,
Antonio Chimienti
2,
Giuseppe Pettiti
2,
Federico Parisi
4,
Gianluigi Ferrari
4,
Nicola Cau
1,
Veronica Cimolin
5,
Corrado Azzaro
1,
Lorenzo Priano
1,3 and
Alessandro Mauro
1,3
1
Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy
2
Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
3
Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
4
CNIT Research Unit of Parma and Department of Information Engineering, University of Parma, 43124 Parma, Italy
5
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(21), 4764; https://doi.org/10.3390/s19214764
Submission received: 29 August 2019 / Revised: 30 October 2019 / Accepted: 31 October 2019 / Published: 2 November 2019
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Abstract

The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson’s disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson’s disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD.
Keywords: Parkinson’s disease; UPDRS assessment; RGB-depth cameras; body sensor networks; hand tracking; human machine interface; machine learning; remote monitoring Parkinson’s disease; UPDRS assessment; RGB-depth cameras; body sensor networks; hand tracking; human machine interface; machine learning; remote monitoring

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MDPI and ACS Style

Albani, G.; Ferraris, C.; Nerino, R.; Chimienti, A.; Pettiti, G.; Parisi, F.; Ferrari, G.; Cau, N.; Cimolin, V.; Azzaro, C.; et al. An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease. Sensors 2019, 19, 4764. https://doi.org/10.3390/s19214764

AMA Style

Albani G, Ferraris C, Nerino R, Chimienti A, Pettiti G, Parisi F, Ferrari G, Cau N, Cimolin V, Azzaro C, et al. An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease. Sensors. 2019; 19(21):4764. https://doi.org/10.3390/s19214764

Chicago/Turabian Style

Albani, Giovanni, Claudia Ferraris, Roberto Nerino, Antonio Chimienti, Giuseppe Pettiti, Federico Parisi, Gianluigi Ferrari, Nicola Cau, Veronica Cimolin, Corrado Azzaro, and et al. 2019. "An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease" Sensors 19, no. 21: 4764. https://doi.org/10.3390/s19214764

APA Style

Albani, G., Ferraris, C., Nerino, R., Chimienti, A., Pettiti, G., Parisi, F., Ferrari, G., Cau, N., Cimolin, V., Azzaro, C., Priano, L., & Mauro, A. (2019). An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease. Sensors, 19(21), 4764. https://doi.org/10.3390/s19214764

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