Yang Xiaodong, Fan Dou, Ren Aifeng, Zhao Nan, Zhang Zhiya, Haider Daniyal, Khan Muhammad Bilal, Tian Jie
1School of Electronic EngineeringXidian UniversityXi'an710071China.
2School of Life Science and TechnologyXidian UniversityXi'an710126China.
IEEE J Transl Eng Health Med. 2019 May 31;7:1800408. doi: 10.1109/JTEHM.2019.2919065. eCollection 2019.
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.
帕金森步态是震颤麻痹（SP）的一个典型特征，与其他SP症状相比，它对人类健康生活的影响更为严重。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，用于对异常步态和正常行走进行分类。由于帕金森病患者的步态与健康人不同，无线信号在接收器处反射并产生不同的变化，可用于SP的诊断和分类。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，将支持向量机（SVM）分类算法应用于收集的数据，以将SP患者的异常步态与正常步态进行分类。我们对不同的人评估了所提出的系统，实验结果表明，这种基于训练的系统对帕金森步态的检测达到了90%以上的高精度。此外，以非接触方式实现了SP的早期预警。
