摘要
冻结步态(FoG)是一种在帕金森病(PD)中常见的异常步态,而拖步则是冻结步态的一种表现形式,也是医生用来判断患者的治疗状况的重要因素,并且拖步状态也对PD患者的日常生活有很大影响。该文提出一种通过计算机视觉来实现患者拖步状态自动识别的方法,该方法通过以3维卷积为基础的网络结构,可以从PD患者的TUG测试视频中自动识别出患者是否具有拖步症状。其思路是首先利用特征提取模块从经过预处理的视频序列中提取出时空特征,然后将得到的特征在不同空间和时间尺度上进行融合,之后将这些特征送入分类网络中得到相应的识别结果。在该工作中共收集364个正常步态样本和362个具有拖步状态的样本来构成实验数据集,在该数据集上的实验表明,该方法的平均准确率能够达到91.3%。并且其能从临床常用的TUG测试视频中自动准确地识别出患者的拖步状态,这也为远程监测帕金森病患者的治疗状态提供了助力。
Freezing of Gait(FoG)is a common symptom among patients with Parkinson’s Disease(PD).In this paper,a vision-based method is proposed to recognize automatically the shuffling step symptom from the Timed Up-and-Go(TUG)videos based.In this method,a feature extraction block is utilized to extract features from image sequences,then features are fused along a temporal dimension,and these features are fed into a classification layer.In this experiment,the dataset with 364 normal gait examples and 362 shuffling step examples is used.And the experiment on the collected dataset shows that the average accuracy of the best method is 91.3%.Using this method,the symptom of the shuffling step can be recognized automatically and efficiently from TUG videos,showing the possibility to remotely monitor the movement condition of PD patients.
作者
陈晓禾
曹旭刚
陈健生
胡春华
马羽
CHEN Xiaohe;CAO Xugang;CHEN Jiansheng;HU Chunhua;MA Yu(Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou 215163,China;Department of Electronic and Information Engineering,Changchun University of Science and Technology,Changchun 130022,China;Department of Electronic Eigneering,Tsinghua University,Beijing 100084,China;School of Aerospace Engineering,Tsinghua University,Beijing 100084,China;Yuquan Hospital,Tsinghua University,Beijing 100040,China;Beijing National Research Center for Information Science and Technology,Beijing 100084,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2021年第12期3467-3475,共9页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61673234)。
关键词
视频序列分析
3维卷积
异常步态识别
拖步识别
Video sequence analysis
3D convolution
Abnormal gait recognition
Shuffling step recognition
