A novel computer vision based gait analysis technique for normal and Parkinson’s gaits classification

Gong, Liyun, Li, Jing, Miao, Yu , Zhu, Ming and Clifford, Ross (2020) A novel computer vision based gait analysis technique for normal and Parkinson’s gaits classification. In: 5th IEEE Cyber Science and Technology Congress, 17-22 Aug. 2020.

Full content URL: https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSci...

Documents
A novel computer vision based gait analysis technique for normal and Parkinson’s gaits classification
Accepted Manuscript
[img] PDF
conference_101719.pdf - Whole Document
Restricted to Repository staff only until 11 November 2022.

14MB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Parkinsons disease (PD) can affect a person’s gait and potentially lead to some gait impairments (e.g., freezing gait, shuffling gait,etc.) for a PD patient. Analyzing a person’s gait characteristics is important for both the early diagnosis and evaluation of their PD. In this work, a novel computer vision based technique is proposed for gait analysis to classify normal or Parkinson’s gaits, by using a normal RGB camera. Based on recorded videos of normal gaits, a mask R-CNN, which is a modern deep neural network for objects segmentation, is applied for extracting human silhouettes from video frames. Gait energy images (GEIs) are then obtained from human silhouettes extracted from video clips of normal gaits and processed as features, which are applied to construct a one class support vector machine (OCSVM) model for normal/PD gaits classification. Comprehensive experimental studies show that the proposed technique can successfully classify normal/PD gaits with a high accuracy of more than 97%.

Keywords:healthcare, mask R-CNN, one class classification
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:42681
Deposited On:17 Dec 2020 19:08

Repository Staff Only: item control page