Non-contact Early Warning of Shaking Palsy

June 18, 2019

Xiaodong YangDou FanAifeng RenNan ZhaoZhiya ZhangDaniyal HaiderMuhammad Bilal KhanJie Tian

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Non-contact Early Warning of Shaking Palsy

Abstract

Objective: Parkinsonian gait is a defining feature of Shaking Palsy (SP) and it has one of the worse impact 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. Method: 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, 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. Results: We evaluate the proposed system with different people, and the experimental outcomes shows the Parkinsonian gait detection of this training-based system achieves a high accuracy of above 90%. Conclusion: the early warning of Shaking Palsy is achieved in a non-contact manner.

READ FULL ARTICLE ON IEEE XPLORE

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