title: Quantitative measurement of motor symptoms in Parkinson's disease: a study with full-body motion capture data.
abstract: Recent advancements in the portability and affordability of optical motion capture systems have opened the doors to various clinical applications. In this paper, we look into the potential use of motion capture data for the quantitative analysis of motor symptoms in Parkinson's Disease (PD). The standard of care, human observer-based assessments of the motor symptoms, can be very subjective and are often inadequate for tracking mild symptoms. Motion capture systems, on the other hand, can potentially provide more objective and quantitative assessments. In this pilot study, we perform full-body motion capture of Parkinson's patients with deep brain stimulator off-drugs and with stimulators on and off. Our experimental results indicate that the quantitative measure on spatio-temporal statistics learnt from the motion capture data reveal distinctive differences between mild and severe symptoms. We used a Support Vector Machine (SVM) classifier for discriminating mild vs. severe symptoms with an average accuracy of approximately 90%. Finally, we conclude that motion capture technology could potentially be an accurate, reliable and effective tool for statistical data mining on motor symptoms related to PD. This would enable us to devise more effective ways to track the progression of neurodegenerative movement disorders.
authors.affiliation: The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Samarjit
authors.initials: S
authors.lastname: Das
authors.forename: Laura
authors.initials: L
authors.lastname: Trutoiu
authors.forename: Akihiko
authors.initials: A
authors.lastname: Murai
authors.forename: Dunbar
authors.initials: D
authors.lastname: Alcindor
authors.forename: Michael
authors.initials: M
authors.lastname: Oh
authors.forename: Fernando
authors.initials: F
authors.lastname: De la Torre
authors.forename: Jessica
authors.initials: J
authors.lastname: Hodgins
mesh_terms: D000054:Acceleration; D000368:Aged; D001185:Artificial Intelligence; D057225:Data Mining; D018450:Disease Progression; D004867:Equipment Design; D005260:Female; D005583:Fourier Analysis; D005684:Gait; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009038:Motion; D009048:Motor Skills; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology*; D004856:Postural Balance; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D014202:Tremor / Q000503:physiopathology