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.

中文翻译：

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非接触式摇晃性麻痹预警

帕金森步态是震颤麻痹 (SP) 的一个定义特征，它对人类健康生活的影响比其他 SP 症状更严重。这项工作的目的是提出一种基于 S 波段感知技术的帕金森步态检测系统，以对异常步态和正常步行进行分类。由于帕金森病患者与健康人的步态差异，无线信号在接收器处反射并产生不同的变化，可用于 SP 诊断和分类。为了检测帕金森步态，我们首先对原始数据进行数据预处理，以获得清晰的幅度和相位信息。然后，通过主成分分析（PCA）进行特征提取。最后，对收集到的数据应用支持向量机 (SVM) 分类算法，对 SP 患者的异常步态与正常步态进行分类。我们用不同的人评估了所提出的系统，实验结果表明，这个基于训练的系统的帕金森步态检测达到了 90% 以上的高精度。而且，以非接触方式实现对SP的预警。