Shuffling Step Recognition Using 3D Convolution for Parkinsonian Patients

Abstract

Freezing of Gait (FoG) is a common symptom among patients with Parkinson’s Disease (PD). In this paper, a vision-based method is proposed to automatically recognize the shuffling step symptom from the Timed Up-and-Go (TUG) videos based. In this method, a feature extraction block is utilized to extract features from image sequences, then features are fused along a temporal dimension, and these features are fed into a classification layer. In this experiment, the dataset with 364 normal gait examples and 362 shuffling step examples is used. And the experiment on the collected dataset shows that the average accuracy of the best method is 91.3%. Using this method, the symptom of the shuffling step can be recognized automatically and efficiently from TUG videos, showing the possibility to remotely monitor the movement condition of PD patients.