Abstract
Falling is a common issue within the aging
population. The immediate detection of a fall is key to
guarantee early and immediate attention to avoid other
potential immobility risks and reduction in recovery
time. Video-based approaches for monitoring fall
detection, although being highly accurate, are largely
perceived as being intrusive if deployed within living
environments. As an alternative, thermal vision-based
methods can be deployed to offer a more acceptable
level of privacy. To date, thermal vision-based fall
detection methods have largely focused on
single-occupancy scenarios, which are not fully
representative of real living environments with
multi-occupancy. This work proposes a non-invasive thermal vision-based approach of multi-occupancy fall detection
(MoT-LoGNN) which discriminates between a fall or no-fall. The approach consists of four major components: i) a
multi-occupancy decomposer, ii) a sensitivity-based sample selector, iii) the T-LoGNN for single-occupancy fall
detection, and iv) a fine-tuning mechanism. The T-LoGNN consists of a robust neural network minimizing a Localized
Generalization Error (L-GEM) and thermal image features extracted by a Convolutional Neural Network (CNN).
Comparing to other methods, the MoT-LoGNN achieved the highest average accuracy of 98.39% within the context of a
multi-occupancy fall detection experiment.
population. The immediate detection of a fall is key to
guarantee early and immediate attention to avoid other
potential immobility risks and reduction in recovery
time. Video-based approaches for monitoring fall
detection, although being highly accurate, are largely
perceived as being intrusive if deployed within living
environments. As an alternative, thermal vision-based
methods can be deployed to offer a more acceptable
level of privacy. To date, thermal vision-based fall
detection methods have largely focused on
single-occupancy scenarios, which are not fully
representative of real living environments with
multi-occupancy. This work proposes a non-invasive thermal vision-based approach of multi-occupancy fall detection
(MoT-LoGNN) which discriminates between a fall or no-fall. The approach consists of four major components: i) a
multi-occupancy decomposer, ii) a sensitivity-based sample selector, iii) the T-LoGNN for single-occupancy fall
detection, and iv) a fine-tuning mechanism. The T-LoGNN consists of a robust neural network minimizing a Localized
Generalization Error (L-GEM) and thermal image features extracted by a Convolutional Neural Network (CNN).
Comparing to other methods, the MoT-LoGNN achieved the highest average accuracy of 98.39% within the context of a
multi-occupancy fall detection experiment.
| Original language | English |
|---|---|
| Article number | 9234482 |
| Pages (from-to) | 5377-5388 |
| Number of pages | 12 |
| Journal | IEEE Sensors Journal |
| Volume | 21 |
| Issue number | 4 |
| Early online date | 21 Oct 2020 |
| DOIs | |
| Publication status | Published - 15 Feb 2021 |
Bibliographical note
Funding Information:Manuscript received September 18, 2020; accepted October 14, 2020. Date of publication October 21, 2020; date of current version January 15, 2021. This work was supported in part by the 2020 Research and Development Program in Key Areas of Guangdong Province under Grant 2020B010166002, in part by the National Natural Science Foundation of China under Grant 61876066, in part by the Guangdong Province Science and Technology Plan Project (Collaborative Innovation and Platform Environment Construction) under Grant 2019A050510006, and the REMIND project funded by Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 under Grant Agreement No. 734355. The associate editor coordinating the review of this article and approving it for publication was Prof. Elena Gaura. (Corresponding author: Wing W. Y. Ng.) Cankun Zhong and Wing W. Y. Ng are with the Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China (e-mail: curran.z@qq.com; wingng@ieee.org).
Publisher Copyright:
© 2001-2012 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords
- Feature extraction
- Image sensors
- MoT-LoGNN
- Multi-occupancy Fall Detection
- Neural Networks
- Perturbation methods
- Sensors
- Temperature sensors
- Thermal Vision Sensor
- Thermal sensors
- Training
- smart environments