Abstract:
Parkinson disease (PD) is a neurological disorder that influences movement of muscles
causing disabled posture, rigidity and tremors. People suffering from PD face trouble
in sleeping, walking and sitting. Generally, potential PD subjects are examined by an
expert medical practitioner who employs the clinical symptoms like stiffness and slowing
movements to detect the presence or absence of the disease, only after the disease had
progressed considerably. Research on PD has revealed that analysis of handwriting and
speech can serve as an effective early warning for Parkinson. With the advancements
in image analysis and pattern classification, the manual analysis of these handwritten
samples is being replaced with computerized analysis and automated prediction systems.
This project presents a system that exploits online features of handwriting to predict PD
in subjects. Features considered in our study include writing speed, pen pressure and
pen-up/pen-down times. The features from PD patients and control subjects are used to
train a support vector machine classifier. Evaluations on a benchmark database of online
handwriting samples realized promising classification rates.
