Name
Title | ||
|---|---|---|
Deep Learning based Gait Analysis for Contactless Dementia Detection System from Video Camera |
Abstract | ||
|---|---|---|
Dementia is a neurodegenerative disease with a high incidence in the elderly. However, there is no effective treatment for this disease, and early intervention has a great effect to slow the deterioration. Currently, the detection of dementia is mainly achieved using questionnaire-like neuropsychological tests. Such ways usually cost a lot of time. To this end, we design a contactless dementia detection system based on gait analysis from surveillance video, and it can serve as a home-based healthcare system. This system applies a Kinect 2.0 camera to capture the human video and extract the skeleton joints at a rate of 15 frames per second. Two different gaits are collected for detection, namely single-task gait and dual-task gait. In this paper, we design a convolutional neural network based classifier to extract features in a data-driven way from these two groups of videos, but not take hand-crafted features. Experimental results show that we achieve a sensitivity of 74.10% on the test set using this system, and the processing only takes several minutes for early dementia detection. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401596 | 2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | DocType | ISSN |
dementia detection, gait analysis, video processing, convolutional neural networks, deep learning | Conference | 0271-4302 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghao Zhang | 1 | 0 | 0.68 |
| Yangyang Jiang | 2 | 0 | 0.68 |
| Xingyu Cao | 3 | 0 | 0.34 |
| Xue Yang | 4 | 0 | 0.68 |
| Ce Zhu | 5 | 1473 | 117.79 |
| Ying Li | 6 | 0 | 0.68 |
| Yipeng Liu | 7 | 43 | 5.93 |