Yan-Min Tang1, Yan-Hong Wang2, Xin-Yu Feng3, Qiao-Sha Zou4, Qing Wang5, Jing Ding6, Richard Chuan-Jin Shi7, Xin Wang8. 1. Department of Neurology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China. Electronic address: 19111210003@fudan.edu.cn. 2. Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China. Electronic address: 18112020009@fudan.edu.cn. 3. Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China. Electronic address: 18212020013@fudan.edu.cn. 4. Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China. Electronic address: qiaoshazou@fudan.edu.cn. 5. Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China. Electronic address: wangqing@ainirobot.com. 6. Department of Neurology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China; Institute of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. Electronic address: ding.jing@zs-hospital.sh.cn. 7. Institute of Brain-inspired Circuits and Systems, Fudan University, 825 Zhangheng Road, Shanghai 201203, China; Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195-3770, USA. Electronic address: cjshi@uw.edu. 8. Department of Neurology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China; Institute of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. Electronic address: wang.xin@zs-hospital.sh.cn.
Abstract
BACKGROUND: Early detection of gait abnormalities is critical for preventing severe injuries in future falls. The timed up and go (TUG) test is a commonly used clinical gait screening test; however, the interpretation of its results is limited to the TUG total time. RESEARCH QUESTION: What is diagnostic accuracy of the low-cost, markerless, automated gait analyzer, with the aid of vision-based artificial intelligence technology, which extract gait spatiotemporal features and screen for abnormal walking patterns through video recordings of the TUG test? METHODS: Our dataset contained retrospective data from outpatients from the Department of Neurology or Rehabilitation of two tertiary hospitals in Shanghai. A panel of three expert neurologists specialized in movement disorders reviewed the gait performance in each TUG video, and labeled them separately, with the most commonly assigned label being used as the reference standard. The gait analyzer performed the AlphaPose algorithm to track the human joint position and calculated the spatiotemporal parameters by filtering and double-threshold signal detection. Gait spatiotemporal features and expert labels were input into machine learning models, and the accuracy of each model was tested with leave-one-out cross-validation (LOOCV). RESULTS: A total of 284 participants were recruited. Among these, 100 were labeled as having abnormal gait performance by experts. The Naive Bayes classifier achieved the best performance with a full-data accuracy of 90.14% and a LOOCV accuracy of 89.08% for screening abnormal gait performance. SIGNIFICANCE: This study is the first to investigate the accuracy of a vision-based intelligent gait analyzer for screening abnormal clinical gait performance. By virtue of a pose estimation algorithm and machine learning models, our intelligent gait analyzer can detect abnormal walking patterns approximate to judgements made by experienced neurologists, which is expected to be a supplementary gait assessment protocol for basic-level doctors in the future.
BACKGROUND: Early detection of gait abnormalities is critical for preventing severe injuries in future falls. The timed up and go (TUG) test is a commonly used clinical gait screening test; however, the interpretation of its results is limited to the TUG total time. RESEARCH QUESTION: What is diagnostic accuracy of the low-cost, markerless, automated gait analyzer, with the aid of vision-based artificial intelligence technology, which extract gait spatiotemporal features and screen for abnormal walking patterns through video recordings of the TUG test? METHODS: Our dataset contained retrospective data from outpatients from the Department of Neurology or Rehabilitation of two tertiary hospitals in Shanghai. A panel of three expert neurologists specialized in movement disorders reviewed the gait performance in each TUG video, and labeled them separately, with the most commonly assigned label being used as the reference standard. The gait analyzer performed the AlphaPose algorithm to track the human joint position and calculated the spatiotemporal parameters by filtering and double-threshold signal detection. Gait spatiotemporal features and expert labels were input into machine learning models, and the accuracy of each model was tested with leave-one-out cross-validation (LOOCV). RESULTS: A total of 284 participants were recruited. Among these, 100 were labeled as having abnormal gait performance by experts. The Naive Bayes classifier achieved the best performance with a full-data accuracy of 90.14% and a LOOCV accuracy of 89.08% for screening abnormal gait performance. SIGNIFICANCE: This study is the first to investigate the accuracy of a vision-based intelligent gait analyzer for screening abnormal clinical gait performance. By virtue of a pose estimation algorithm and machine learning models, our intelligent gait analyzer can detect abnormal walking patterns approximate to judgements made by experienced neurologists, which is expected to be a supplementary gait assessment protocol for basic-level doctors in the future.