Multi-Source Gait Data Acquisition and Feature Recognition Method Based on Wearable Device for Parkinson's Disease

XIE Junxiao; ZHAO Huan; CAO Junyi; CAO Hongmei; QU Qiumin; LIAO Wei-Hsin

doi:10.7652/xjtuxb202306004

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Multi-Source Gait Data Acquisition and Feature Recognition Method Based on Wearable Device for Parkinson's Disease

更新时间：2025-05-08

- Multi-Source Gait Data Acquisition and Feature Recognition Method Based on Wearable Device for Parkinson's Disease
- Vol. 57, Issue 6, Pages: 29-38(2023)
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学第一附属医院神经内科,西安,710061
  3. 香港中文大学机械与自动化工程学系,香港,999077
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202306004
  CLC： TH823;TP274
- Online First：10 June 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
XIE Junxiao, ZHAO Huan, CAO Junyi, et al. Multi-Source Gait Data Acquisition and Feature Recognition Method Based on Wearable Device for Parkinson's Disease[J]. 2023, 57(6): 29-38.
DOI：

XIE Junxiao, ZHAO Huan, CAO Junyi, et al. Multi-Source Gait Data Acquisition and Feature Recognition Method Based on Wearable Device for Parkinson's Disease[J]. 2023, 57(6): 29-38. DOI： 10.7652/xjtuxb202306004.

摘要

针对帕金森病患者因早期症状隐匿、个体差异显著而导致该病早期识别与健康量化表征困难的问题

提出了一种柔性可穿戴的多源步态数据采集与特征分类方法。开发了柔性可穿戴步态采集系统

通过融合16通道柔性压阻传感器与8通道柔性压电传感器分别采集足底压力和形变信息

并针对各传感器设计了多源信号调理和传输电路

实现了多源步态信号的实时采集与无线传输。利用所设计的系统采集了帕金森患者与健康对照组的正常行走数据

通过对步态数据进行特征构建和显著性差异分析

提出了帕金森病特征识别方法

获取了具有识别效果的显著差异性特征。研究结果表明:帕金森病患者的时相特征不对称度、峰值变异系数、步态周期变异系数等特征值明显大于健康对照组。以支持向量机和k-近邻作为分类器分别对比了单一数据和多源数据特征用于帕金森病诊断的结果

发现当同时使用2种信号特征时

分类准确率比单一数据特征高6%～10%

其中基于k-近邻算法的帕金森病分类准确率达到89.66%。所提方法能有效提高帕金森病识别率

为帕金森病步态异常量化分析与诊断的临床应用奠定了基础。

Abstract

As early symptoms of Parkinson's disease patients are hidden

and distinct individual differences make it more difficult to identify early gait abnormalities and characterize healthy states quantitatively

a multi-source gait data acquisition and feature recognition method based on a wearable device was proposed in this paper. The multi-source gait data acquisition system can collect plantar pressure and deformation by fusing a 16-channel flexible piezoresistive sensor and an 8-channel flexible piezoelectric sensor. Through the design of the signal conditioning circuit

the real-time acquisition and wireless transmission of multi-source signals were achieved. Based on the designed system

the gait data of Parkinson's disease patients and healthy controls were collected. A method of feature recognition for Parkinson's disease was proposed based on distinctive features extracted by feature construction and significant difference analysis of gait data. The results showed that the features of Parkinson's disease patients

including asymmetry of phase features

coefficient of variation of peak value

and coefficient of variation of the gait cycle

were more distinctive than those of healthy controls. In addition

the Support Vector Machine and k-nearest neighbor were employed as classifiers to compare the accuracy between single and multi-source data features for diagnosing Parkinson's disease. The classification accuracy of multi-source gait signals was 6% to 10% higher than that of a single signal

in which the k-nearest neighbor classification accuracy of Parkinson's disease reached 89.66%. More importantly

the results demonstrated that the proposed method can effectively improve the ability to recognize Parkinson's disease and lay a foundation for quantitative analysis and diagnosis of Parkinson's disease in clinical application.

关键词

Keywords

references

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