Observatorio de I+D+i UPM

Descripción
The current work describes a methodology to automatically detect the severity of bradykinesia in motor disease patients using wireless, wearable accelerometers. This methodology was tested with cross validation through a sample of 20 Parkinson¿s disease patients. The assessment of methodology was carried out through some daily living activities which were detected using an activity recognition algorithm. The Unified Parkinson¿s Disease Rating Scale (UPDRS) severity classification of the algorithm coincides between 70 and 86% from that of a trained neurologist depending on the classifier used. These severities were calculated for 5 second segments of the signal with 50% of overlap. A bradykinesia profiler is also presented in this work. This profiler removes the overlap of the segments and calculates the confidence of the resulting events. It also calculates average severity, duration and symmetry values for those events. The profiler has been tested with a bogus dataset. Future work includes better training for the severity classifier with a larger sample and testing the profiler with real, longterm patient data in a projected pilot phase in three European hospitals.
Internacional	Si
Nombre congreso
Tipo de participación	960
Lugar del congreso	Buenos Aires, Argentina
Revisores	Si
ISBN o ISSN	978-1-4244-4124-2
DOI
Fecha inicio congreso	31/08/2010
Fecha fin congreso	04/09/2010
Desde la página	1008
Hasta la página	1011
Título de las actas	Abstract Book of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) Merging Medical Humanism and Technology EMBC 2010

Esta actividad pertenece a memorias de investigación

• Autor: Jorge Cancela Gonzalez (UPM)
• Autor: Mario Pansera . (UPM)
• Autor: Maria Teresa Arredondo Waldmeyer (UPM)
• Autor: Juan Jacobo Estrada Paz (UPM)
• Autor: Matteo Pastorino . (UPM)
• Autor: Laura Pastor Sanz (UPM)
• Autor: Juan Luis Villalar González (UPM)

• Creador: Grupo de Investigación: Life Supporting Technologies (Tecnologías de Apoyo a la Vida)