Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

Abstract

Parkinson's disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson's disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson's gait properties without affecting the common behavioral patterns during the clinical practices. Therefore, the Accelerometer Sensor-based Parkinson's Disease Identification System (ASPDIS) is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease. consequently, the proposed classifier can easily predict various severity levels of Parkinson's disease from the sensor data. The performance of the proposed classifier is compared against the existing models such as random forest, decision tree, and k-nearest neighbor classifiers respectively. As per the experimental observation, the proposed classifier has more capability to differentiate Parkinson's from non-Parkinson patients depending upon the severity levels. Also, it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.