title: Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson's Disease.
abstract: This paper is focused on the analysis of data obtained from wearable sensors in patients with Parkinson's Disease. We implemented Support Vector Machines (SVM's) to predict clinical scores of the severity of Parkinsonian symptoms and motor complications. We determined the optimal window length to extract features from the sensor data. Furthermore, we performed tests to determine optimal parameters for the SVM's. Finally, we analyzed how well individual tasks performed by patients captured the severity of various symptoms and motor complications.
authors.affiliation: Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA 02114, USA. spatel19@partners.org
authors.forename: Shyamal
authors.initials: S
authors.lastname: Patel
authors.forename: Richard
authors.initials: R
authors.lastname: Hughes
authors.forename: Nancy
authors.initials: N
authors.lastname: Huggins
authors.forename: David
authors.initials: D
authors.lastname: Standaert
authors.forename: John
authors.initials: J
authors.lastname: Growdon
authors.forename: Jennifer
authors.initials: J
authors.lastname: Dy
authors.forename: Paolo
authors.initials: P
authors.lastname: Bonato
mesh_terms: D000054:Acceleration; D000368:Aged; D000465:Algorithms; D003020:Clothing; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D006801:Humans; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology*; D015203:Reproducibility of Results; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods; D013686:Telemetry / Q000295:instrumentation* / Q000379:methods; D014159:Transducers