WEARABLE WIRELESS EMBEDDED SYSTEM FOR THE DETECTION OF SURFACE ELECTROMYOGRAPHY SIGNALS IN THE PARKINSON'S DISEASE

Anno
2017
Proponente Fernanda Irrera - Professore Ordinario
Sottosettore ERC del proponente del progetto
Componenti gruppo di ricerca
Componente Categoria
Abdallah Cheikh Dottorando/Assegnista/Specializzando componente il gruppo di ricerca / PhD/Assegnista/Specializzando member of the research group
Fabrizio Palma Componenti il gruppo di ricerca / Participants in the research project
Francesco Menichelli Componenti il gruppo di ricerca / Participants in the research project
Abstract

Wearables devices and body sensor networks are attracting most attention for the treatment of chronic diseases of elderly, which need continuous monitoring of the symptoms and adjustment of the therapy. In the Parkinson's Disease (PD) wearables based on inertial sensors can be very useful for performing an objective detection and classification of the motion disorders of different segments of the body. The PI of the present project has been leading a research on this specific topic, which was recipient in 2015 of an international award. The outcomes of that research are the starting point of this proposal. The accuracy of that wearable system based on inertial sensors only can be improved by completing the IMU with the study of the muscle activity. Cramps, muscle contractions, abnormalities in neuromuscular functions and a variety of symptoms related to dyskinesia in the progression of the PD can be studied quantitatively by capturing the muscle activity by surface electromyography (sEMG). The sEMG is a diagnostic technique that detects in real time the muscle activity using patch electrodes properly positioned on the skin along the studied muscle. Today it is performed by the means of cumbersome wired ambulatory equipment, which exhibits electrical artifacts and movement limitations due to wires, and noise at 50 Hz, i.e., in the most meaningful portion of the spectrum. To overcome those problems, here we propose to develop a system aiming to monitoring the motor status of PD patients at home for long time. It integrates in the same board the inertial sensors and algorithms based on inertial data analysis previously developed by the PI, together with sEMG circuits and algorithms for the sEMG data analysis, which are the core of the present project. The novel sensor solution will perform continuous, non-invasive measurements of PD patients conditions with high accuracy, long-time battery autonomy, and small form factor for wearable applications.

ERC
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