A comprehensive motor symptom monitoring and management system: The bradykinesia case

TY - GEN

T1 - A comprehensive motor symptom monitoring and management system

T2 - 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

AU - Cancela, J.

AU - Pansera, M.

AU - Arredondo, M. T.

AU - Estrada, J. J.

AU - Pastorino, M.

AU - Pastor-Sanz, L.

AU - Villalar, J. L.

PY - 2010/12/1

Y1 - 2010/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78650830404&partnerID=8YFLogxK

U2 - 10.1109/iembs.2010.5627775

DO - 10.1109/iembs.2010.5627775

M3 - Article in proceedings

C2 - 21096992

AN - SCOPUS:78650830404

SN - 9781424441235

T3 - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

SP - 1008

EP - 1011

BT - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Y2 - 31 August 2010 through 4 September 2010

ER -