Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer
Identification of activities of daily living is essential in order to evaluate the quality of life both in the elderly and patients with mobility problems. Posture transitions (PT) are one of the most mechanically demanding activities in daily life and,thus, they can lead to falls in patients with m...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| 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:2117/28432 |
| Acceso en línea: | https://hdl.handle.net/2117/28432 https://dx.doi.org/10.1016/j.neucom.2014.09.084 |
| Access Level: | acceso abierto |
| Palabra clave: | Medical electronics Accelerometer Posture Transitions Parkinson's Disease Support Vector Machines Acceleròmetres Parkinson, Malaltia de Electrònica mèdica -- Aparells i instruments Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica |
| Sumario: | Identification of activities of daily living is essential in order to evaluate the quality of life both in the elderly and patients with mobility problems. Posture transitions (PT) are one of the most mechanically demanding activities in daily life and,thus, they can lead to falls in patients with mobility problems. This paper deals with PT recognition in Parkinson’s Disease (PD) patients by means of a triaxial accelerometer situated between the anterior and the left lateral part of the waist. Since sensor’s orientation is susceptible to change during long monitoring periods, a hierarchical structure of classifiers is proposed in order to identify PT while allowing such orientation changes. Results are presented based on signals obtained from 20 PD patients and 67 healthy people who wore an inertial sensor on different positions among the anterior and the left lateral part of the waist. The algorithm has been compared to a previous approach in which only the anterior-lateral location was analyzed improving the sensitivity while preserving specificity. Moreover, different supervised machine l earning techniques have been evaluated in distinguishing PT. Results show that the location of the sensor slightly affects method’s performance and, furthermore, PD motor state does not alter its accuracy. |
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