Non-contact early warning of shaking palsy

Abstract

Parkinsonian gait is a defining feature of shaking palsy (SP) and it has one of the worse impacts on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system based on an S-band perception technique to classify abnormal gait and normal walking. Due to the differences in the Gaits of Parkinson's patients compared with healthy persons, the wireless signals reflect and generates different variations at the receiver that could be used for SP diagnosis and classification. To detect a Parkinsonian gait, we first implement data preprocessing of the original data to obtain clear amplitude and phase information. Then, the feature extraction is carried out by principal component analysis (PCA). Finally, a support vector machine (SVM) classification algorithm is applied on collected data to classify the abnormal gait of SP patients compared with a normal gait. We evaluate the proposed system with different people, and the experimental outcomes show that the Parkinsonian gait detection of this training-based system achieves a high accuracy of above 90%. Moreover, the early warning of SP is achieved in a non-contact manner.