Chin, Wei Hong and Tay, Noel Nuo Wi and Kubota, Naoyuki and Loo, Chu Kiong (2020) A lightweight neural-net with assistive mobile robot for human fall detection system. In: International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), 19-24 July 2020, Online.
Full text not available from this repository.Abstract
Falls are a major health issue, particularly among the elderly. Increasing fall events require high service quality and dedicated medical treatment which is an economic burden. In the lack of appropriate care and support, serious injuries caused by fall will cost lives. Therefore, tracking systems with fall detection capabilities are required. Static-view sensors with machine learning techniques for human fall detection have been widely studied and achieved significant results. However, these systems unable to monitor a person if he or she is out of viewing angle which greatly impedes its performance. Mobile robots are an alternative for keeping the person in sight. However, existing mobile robots are unable to operate for a long time due to battery issues and movement constraints in complex environments. In this paper, we proposed a lightweight deep learning vision-based model for human fall detection with an assistive robot to provide assistance when a fall happens. The proposed detection system requires less computational power which can be implemented in a low-cost 2D camera and GPU board for real-time monitoring. The assistive robot equipped with various sensors that can perform SLAM, obstacle avoidance and navigation autonomously. Our proposed system integrates these two sub-systems to compensate for the weakness of each other to constitute a system that robust, adaptable, and high performance. The proposed method has been validated through a series of experiments.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Universiti Malaya (GC003A-14HTM), Office of Naval Research (IF017-2018), MESTECC, Malaysia (IF0318M1006) |
| Additional Information: | International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), Online, Jul 19-24, 2020 |
| Uncontrolled Keywords: | Fall detection; Deep learning; Assistive robot |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 16 Jun 2023 07:13 |
| Last Modified: | 16 Jun 2023 07:13 |
| URI: | http://eprints.um.edu.my/id/eprint/36999 |
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