目的 开发一种基于深度图像的非接触式帕金森病步态不对称性识别方法,以辅助医疗诊断和评估,解决穿戴型传感设备费用高、影响正常生活且检查流程复杂的问题。方法 2016年7月至8月,对帕金森病患者8例和健康人10例,采用Kinect V2.0采集行走6 m的运动数据;对左右脚参数滤波处理后分别聚类,使用相似度矩阵算法分别计算健康人和帕金森病患者相似度值;使用隐马尔科夫模型验证该方法的识别效果。结果 所有患者左右脚参数聚类序列相似度小于健康人;从患者中提取的14条数据,成功识别12条(85.71%);从健康人中提取的46条数据,成功区别35条(76.09%)。结论 基于左右脚位移过程中步态参数聚类结果不对称性的非接触式识别方法,对于帕金森病患者有一定识别效果。
Objective To develop a non-contact identification method for gait asymmetry in Parkinson's disease based on depth image to assist medical diagnosis and assessment, to avoid the cost, impact on normal life, and complex process of high wear-out sensing equipment. Methods From July to August, 2016, eight patients with Parkinson's disease and ten healthy subjects were collected the gait parameters of walking six meters with Kinect V2.0. The parameters of left and right foot were filtered and clustered. Then similarity matrix algorithm was used to find the difference between healthy subject and patient similarity values. Finally, the recognition effect of this method was verified by Hidden Markov Model. Results The similarity of clustering sequences of left and right foot parameters was less in the patients than in the healthy individuals. There were twelve of 14 data identified in patients, and 35 of 46 in the healthy. Conclusion A non-contact identification method for the asymmetry of gait has been developed based on the parameter clustering results of left and right foot, which is some effective on identifying Parkinson's patients.
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