An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease
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 decen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/21/4764 |
| _version_ | 1798041105801936896 |
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| author | Giovanni Albani Claudia Ferraris Roberto Nerino Antonio Chimienti Giuseppe Pettiti Federico Parisi Gianluigi Ferrari Nicola Cau Veronica Cimolin Corrado Azzaro Lorenzo Priano Alessandro Mauro |
| author_facet | Giovanni Albani Claudia Ferraris Roberto Nerino Antonio Chimienti Giuseppe Pettiti Federico Parisi Gianluigi Ferrari Nicola Cau Veronica Cimolin Corrado Azzaro Lorenzo Priano Alessandro Mauro |
| author_sort | Giovanni Albani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:16:51Z |
| format | Article |
| id | doaj.art-fc838fd231ab4160a034ef0f692c61b8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:16:51Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fc838fd231ab4160a034ef0f692c61b82022-12-22T04:00:21ZengMDPI AGSensors1424-82202019-11-011921476410.3390/s19214764s19214764An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s DiseaseGiovanni Albani0Claudia Ferraris1Roberto Nerino2Antonio Chimienti3Giuseppe Pettiti4Federico Parisi5Gianluigi Ferrari6Nicola Cau7Veronica Cimolin8Corrado Azzaro9Lorenzo Priano10Alessandro Mauro11Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), ItalyInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyCNIT Research Unit of Parma and Department of Information Engineering, University of Parma, 43124 Parma, ItalyCNIT Research Unit of Parma and Department of Information Engineering, University of Parma, 43124 Parma, ItalyIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, ItalyIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), ItalyIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), ItalyIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), ItalyThe 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.https://www.mdpi.com/1424-8220/19/21/4764parkinson’s diseaseupdrs assessmentrgb-depth camerasbody sensor networkshand trackinghuman machine interfacemachine learningremote monitoring |
| spellingShingle | Giovanni Albani Claudia Ferraris Roberto Nerino Antonio Chimienti Giuseppe Pettiti Federico Parisi Gianluigi Ferrari Nicola Cau Veronica Cimolin Corrado Azzaro Lorenzo Priano Alessandro Mauro An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease Sensors parkinson’s disease updrs assessment rgb-depth cameras body sensor networks hand tracking human machine interface machine learning remote monitoring |
| title | An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease |
| title_full | An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease |
| title_fullStr | An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease |
| title_full_unstemmed | An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease |
| title_short | An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease |
| title_sort | integrated multi sensor approach for the remote monitoring of parkinson s disease |
| topic | parkinson’s disease updrs assessment rgb-depth cameras body sensor networks hand tracking human machine interface machine learning remote monitoring |
| url | https://www.mdpi.com/1424-8220/19/21/4764 |
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