Detection of Gait Abnormalities caused by Neurological Disorders

TL;DR

This paper introduces a method for detecting neurological disorders through gait analysis by extracting features from human pose keypoints and validating them on a synthetic dataset, demonstrating successful abnormality detection.

Contribution

The paper presents novel gait features derived from pose keypoints and a synthetic dataset for detecting neurological disorder-related gait abnormalities.

Findings

Gait features effectively detect abnormalities associated with neurological disorders.

Synthetic dataset extit{NeuroSynGait} enables evaluation of gait-based detection methods.

Successful demonstration of abnormal gait detection in experiments.

Abstract

In this paper, we leverage gait to potentially detect some of the important neurological disorders, namely Parkinson's disease, Diplegia, Hemiplegia, and Huntington's Chorea. Persons with these neurological disorders often have a very abnormal gait, which motivates us to target gait for their potential detection. Some of the abnormalities involve the circumduction of legs, forward-bending, involuntary movements, etc. To detect such abnormalities in gait, we develop gait features from the key-points of the human pose, namely shoulders, elbows, hips, knees, ankles, etc. To evaluate the effectiveness of our gait features in detecting the abnormalities related to these diseases, we build a synthetic video dataset of persons mimicking the gait of persons with such disorders, considering the difficulty in finding a sufficient number of people with these disorders. We name it \textit{NeuroSynGait} video dataset. Experiments demonstrated that our gait features were indeed successful in detecting these abnormalities.