The assessment of tremor features of subjects affected by Parkinson's disease supports physicians in defining customized rehabilitation treatment which, in turn, can lead to better clinical outcome. In the standard assessment protocol patient performed many exercises that are useful to physicians to rate disease. But the rating is subjective since is based on an observational evaluation. In this paper, we introduce a novel method for achieving objective assessment of movement conditions by directly measuring the magnitude of involuntary tremors with a set of sensors. We focused on one of the standard tasks of the Unified Parkinson's Disease Rating Scale: finger-to-nose maneuver. During the task, data related to patient finger position are stored and then some tremor's features are extracted. Finally, we employ a Support Vector Machine to measure the relevance of the extracted features in classify healthy subjects and patients.

A RGB-D sensor based tool for assessment and rating of movement disorders

Bevilacqua, Vitoantonio;Trotta, Gianpaolo Francesco;Loconsole, Claudio;Brunetti, Antonio;Caporusso, Nicholas;
2017

Abstract

The assessment of tremor features of subjects affected by Parkinson's disease supports physicians in defining customized rehabilitation treatment which, in turn, can lead to better clinical outcome. In the standard assessment protocol patient performed many exercises that are useful to physicians to rate disease. But the rating is subjective since is based on an observational evaluation. In this paper, we introduce a novel method for achieving objective assessment of movement conditions by directly measuring the magnitude of involuntary tremors with a set of sensors. We focused on one of the standard tasks of the Unified Parkinson's Disease Rating Scale: finger-to-nose maneuver. During the task, data related to patient finger position are stored and then some tremor's features are extracted. Finally, we employ a Support Vector Machine to measure the relevance of the extracted features in classify healthy subjects and patients.
AHFE 2017: International Conferences on Human Factors and Ergonomics in Healthcare and Medical Devices
978-3-319-60482-4
978-3-319-60483-1
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/112309
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