A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions - A Deep Learning Approach

Rachneet Kaur, Robert W. Motl, Richard Sowers, Manuel E. Hernandez

Research output: Contribution to journalArticlepeer-review


This study examined the effectiveness of a v ision-based framework for m ultiple s clerosis (MS) and Parkinson's disease (PD) gait dysfunction prediction. We collected gait video data from multi-view digital cameras during self-paced walking from MS, PD patients and age, weight, height and gender-matched healthy older adults (HOA). We then extracted characteristic 3D joint keypoints from the collected videos. In this work, we proposed a data-driven methodology to classify strides in persons with MS (PwMS), persons with PD (PwPD) and HOA that may generalize across different walking tasks and subjects. We presented a comprehensive quantitative comparison of 16 diverse traditional machine and deep learning (DL) algorithms. When generalizing from comfortable walking (W) to walking-while-talking (WT), multi-scale residual neural network achieved perfect accuracy and AUC for classifying individuals with a given gait disorder; for subject generalization in W trials, residual neural network resulted in the highest accuracy and AUC of 78.1% and 0.87 (resp.), and 1D convolutional neural network (CNN) had highest accuracy of 75% in WT trials. Finally, when generalizing over new subjects in different tasks, again 1D CNN had the top classification accuracy and AUC of 79.3% and 0.93 (resp.). This work is the first attempt to apply and demonstrate the potential of DL with a multi-view digital camera-based gait analysis framework for neurological gait dysfunction prediction. This study suggests the viability of inexpensive vision-based systems for diagnosing certain neurological disorders.
Original languageEnglish (US)
Pages (from-to)190-201
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Early online dateSep 20 2022
StatePublished - Jan 1 2023


  • Deep learning
  • Digital cameras
  • Feature extraction
  • Foot
  • Gait videos
  • Legged locomotion
  • Multiple sclerosis
  • Parkinson's disease
  • Pose estimation
  • Pulse width modulation
  • Task analysis
  • Three-dimensional displays
  • deep learning
  • pose estimation
  • gait videos

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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