Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson's Disease.
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.
Patel
Shyamal
S
Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA 02114, USA. spatel19@partners.org
Hughes
Richard
R
Huggins
Nancy
N
Standaert
David
D
Growdon
John
J
Dy
Jennifer
J
Bonato
Paolo
P
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