Automatic recognition of freezing of gait in Parkinson's disease based on mobile video
10.3760/cma.j.cn115354-20211119-00751
- VernacularTitle:基于手机视频的帕金森病患者冻结步态的自动识别
- Author:
Wendan LI
1
;
Xiujun CHEN
;
Mengyan LI
;
Zhonglue CHEN
;
Hongmin BAI
;
Jiajia WANG
;
Hanqiang DU
;
Haiqiang ZOU
Author Information
1. 广州中医药大学研究生院，广州 510006
- Keywords:
Parkinson's disease;
Freezing of gait;
Machine vision;
Machine learning
- From:
Chinese Journal of Neuromedicine
2022;21(4):348-353
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct an automatic recognition system for PD patients with freezing of gait (FOG) based on mobile phone videos by recording the gait videos of PD patients with FOG.Methods:Forty-nine PD patients with FOG, admitted to our hospital from December 2020 to May 2021, were chosen in our study. Their clinical data were collected. The processes of these patients accepted "3-meter-round trip" and "3-meter-round trip through narrow (0.6 m)" were recorded and 87 valid gait videos were extracted. Position signals of key points in the video were extracted, and featured data were extracted after signal preprocessing. From the featured data, action recognition model, straight FOG recognition model and turn FOG recognition model were established respectively, and finally end-to-end FOG recognition model was formed. Leave-one-subject-out (LOSO) method was used to evaluate the performance of the above models.Results:A total of 22 066 non-FOG window samples and 3815 FOG window samples were obtained from 87 valid videos, which constituted the training sample pool of this study. LOSO method showed that the motion recognition model enjoyed 83.27% sensitivity, 91.38% specificity, and 89.28% accuracy; the straight FOG recognition model enjoyed 57.69% sensitivity and 88.12% specificity; the turn FOG recognition model enjoyed 61.54% sensitivity and specificity 98.72%; and the end-to-end FOG recognition model enjoyed 85.71% sensitivity and 75.73% specificity.Conclusion:The automatic recognition system for PD patients with FOG based on mobile phone videos has relatively high sensitivity and specificity, which can realize remote assessment and is convenient for screening and follow-up of PD patients with FOG.
