A waist-worn inertial measurement unit for Parkinson’s disease long-term monitoring
Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have demonstrated to accurately monitor motor symptoms of Parkinson’s disease (PD). In this sense, most of previous works have attempted to...
| Autores: | , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/104742 |
| Acceso en línea: | https://hdl.handle.net/2117/104742 https://dx.doi.org/10.3390/s17040827 |
| Access Level: | acceso abierto |
| Palabra clave: | Parkinson's disease Patient monitoring Inertial Measurement Unit Parkinson’s Disease Monitoring Inertial Data Capture Algorithm Parkinson, Malaltia de Monitoratge de pacients -- Aparells i accessoris Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors |
| Sumario: | Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have demonstrated to accurately monitor motor symptoms of Parkinson’s disease (PD). In this sense, most of previous works have attempted to assess PD symptoms through IMUs in controlled environments or short tests. This paper presents the design of an IMU called 9x3 that aims to assess PD symptoms, enabling the possibility to perform a map of patients’ symptoms at their homes during long periods of time. The designed device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU also enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9x3, capable of storing inertial information and algorithm outputs, sending messages to external devices. This paper also presents the results of detecting freezing of gait and brad kinetic gait in 12 patients, with sensitivity and specificity above 80%. Additionally, the system enables working 23.09 days (at waking hours) with a 1200mAh battery sampling at 50 Hz, opening up the possibility to be employed at other applications like wellbeing and sports. |
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