Dynamic SVM detection of tremor and dyskinesia during unscripted and unconstrained activities.
In this paper, we report an experimental comparison of dynamic support vector machines (SVMs) to dynamic neural networks (DNNs) in the context of a system for detecting dyskinesia and tremor in Parkinson's disease (PD) patients wearing accelerometer (ACC) and surface electromyographic (sEMG) sensors while performing unscripted and unconstrained activities of daily living. These results indicate that SVMs and DNNs of comparable computational complexities yield approximately identical performance levels when using an identical set of input features.
Dept. of Electrical and Computer Engineering ECE, Boston University, Boston, MA 02215, USA. bryan.cole@draeger.com
Bryan T
BT
Cole
Pinar
P
Ozdemir
S Hamid
SH
Nawab
D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020820:Dyskinesias / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014202:Tremor / Q000175:diagnosis* / Q000209:etiology