A Video-Based Method to Classify Abnormal Gait for Remote Screening of Parkinson's Disease

Telemedicine is of growing importance for the increasing number of patients with population aging, lack of medical resources in the countryside, and special situation such as the COVID-19 pandemic. Gait impairment is a major motor symptom for patients suffering from neurological disorders such as Parkinson's disease (PD), and serves as an important indicator for early screening and diagnosis of the disease. Existing gait analysis methods typically require advanced equipment, trained professionals, and complex procedures. In this paper, we propose a method to classify the abnormal gait of patients suffering from Parkinson's disease and normal gait, solely with the 2D walking videos recorded by a common camera or a smartphone. A pose estimation algorithm is employed to extract the skeleton of the subjects from the videos. Based on the analysis of motor disturbances resulting from Parkinson's disease, specific gait features are defined and extracted, including step length, walking speed, arm swing magnitude, and velocity. Considering that the sample size of clinical data is limited in the early stage, classic classifiers are applied, including logistic regression (LR), support vector machine (SVM), and random forest (RF). The registered clinical study was conducted with 20 PD patients and 20 age-matched healthy controls. With the three classifiers, 87.5% (LR), 90.0% (SVM), and 92.5% (RF) of classification accuracy were achieved, respectively. Thus, video-based classification of abnormal gait promises a solution for remote screening and diagnosis of neurological diseases.