The main objective of this study is the realization of advanced methods of automatic evaluation and continuous monitoring tools for the support to the medical evaluation of Parkinson’s disease (PD) and to the follow up of the patient also through the presentation and classification of data through a universally recognized clinical scale. We have developed an embedded wearable device integrating a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer, realizing a sensor with embedded data fusion and aggregation algorithms. Two different classification and detection algorithms are implemented to provide a comprehensive description and objective quantification of the two most investigated PD’s symptoms as tremor and freezing of gait (FoG). The system is usable in clinical and diagnostic settings showing its effectiveness above all in patients home monitoring through the upload in Cloud and the real-time processing of the acquired data. In the latter case, it allows the detection and objectification of PD symptoms manifested by the patient in everyday life, thereby providing the doctor with essential information for the dosage and effective personalization of drug therapy. For the system validation, a series of tests were carried out according to a defined experimental protocol on a sample of PD patients and healthy subjects with the presence of a video recording system and under the supervision of a neurologists team. The results obtained show a system’s accuracy of 97.7% for tremor classification and 99.7% for FoG events detection.

A Smart Inertial System for 24h Monitoring and Classification of Tremor and Freezing of Gait in Parkinson’s Disease / Pierleoni, Paola; Belli, Alberto; Bazgir, Omid; Maurizi, Lorenzo; Paniccia, Michele; Palma, Lorenzo. - In: IEEE SENSORS JOURNAL. - ISSN 1530-437X. - ELETTRONICO. - 19:(2019), pp. 11612-11623. [10.1109/JSEN.2019.2932584]

A Smart Inertial System for 24h Monitoring and Classification of Tremor and Freezing of Gait in Parkinson’s Disease

Paola Pierleoni;Alberto Belli;Lorenzo Maurizi;Lorenzo Palma
2019-01-01

Abstract

The main objective of this study is the realization of advanced methods of automatic evaluation and continuous monitoring tools for the support to the medical evaluation of Parkinson’s disease (PD) and to the follow up of the patient also through the presentation and classification of data through a universally recognized clinical scale. We have developed an embedded wearable device integrating a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer, realizing a sensor with embedded data fusion and aggregation algorithms. Two different classification and detection algorithms are implemented to provide a comprehensive description and objective quantification of the two most investigated PD’s symptoms as tremor and freezing of gait (FoG). The system is usable in clinical and diagnostic settings showing its effectiveness above all in patients home monitoring through the upload in Cloud and the real-time processing of the acquired data. In the latter case, it allows the detection and objectification of PD symptoms manifested by the patient in everyday life, thereby providing the doctor with essential information for the dosage and effective personalization of drug therapy. For the system validation, a series of tests were carried out according to a defined experimental protocol on a sample of PD patients and healthy subjects with the presence of a video recording system and under the supervision of a neurologists team. The results obtained show a system’s accuracy of 97.7% for tremor classification and 99.7% for FoG events detection.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/271176
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