Nintendo Wii assessment of Hoehn and Yahr score with Parkinson's disease tremor

Abstract

OBJECTIVE:

Diagnosis of Parkinson's Disease (PD) by analyzing the resting tremor were much studied by using different accelerometer based methods, however the quantitative assessment of Hoehn and Yahr Scale (HYS) score with a machine learning based system has not been previously addressed. In this study, we aimed to propose a system to automatically assess the HYS score of patients with PD.

METHODS:

The system was evaluated and tested on a dataset containing 55 subjects where 35 of them were patients and 20 of them were healthy controls. The resting tremor data were gathered with the 3 axis accelerometer of the Nintendo Wii (Wiimote). The clinical disability of the PD was graded from 1 to 5 by the HYS and tremor was recorded twice from the more affected side in each patient and from the dominant extremity in each control for a 60 seconds period. The HYS scores were learned with Support Vector Machines (SVM) from the features of the tremor data.

RESULTS:

Thirty-two of the subjects with PD were classified correctly and 18 of the normal subjects were also classified correctly by our system. The system had average 0.89 accuracy rate (Range: 81-100% changing according to grading by HYS).

CONCLUSIONS:

We compared quantitative measurements of hand tremor in PD patients, with staging of PD based on accelerometer data gathered using the Wii sensor. Our results showed that the machine learning based system with simple features could be helpful for diagnosis of PD and estimate HYS score. We believed that this portable and easy-to-use Wii sensor measure might also be applicable in the continuous monitoring of the resting tremor with small modifications in routine clinical use.