Deep-Learning Technique for Risk-Based Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array

Eldering caring is important in today's aging society, especially that accident anticipation/prevention plays an important role. In this paper, a novel approach to preventing elderly accidents based on a very low-resolution thermopile sensor array (TPA) (only 32\times 32 pixels) is proposed f...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2023-06, Vol.33 (6), p.2852-2863
Main Authors: Morawski, Igor, Lie, Wen-Nung, Aing, Lee, Chiang, Jui-Chiu, Chen, Kuan-Ting
Format: Article
Language:English
Subjects:
Accidents
Action prediction
Artificial neural networks
Cameras
Deep learning
elderly monitoring
low resolution
Machine learning
Monitoring
Neural networks
Older adults
Older people
privacy
Real time operation
Recurrent neural networks
Sensor arrays
Sensors
Sociology
Statistics
Task analysis
thermopile sensor array
Thermopiles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Eldering caring is important in today's aging society, especially that accident anticipation/prevention plays an important role. In this paper, a novel approach to preventing elderly accidents based on a very low-resolution thermopile sensor array (TPA) (only 32\times 32 pixels) is proposed for prediction of bed-exit event that might lead to elderly falls in home caring. Low-resolution TPA sensor, capable of collecting far infrared energy, ensures cost-effective monitoring, no interference with user's daily life, and most importantly privacy-preservation. Since most of the fall accidents occur when the elderly attempts to get off the bed without assistance, it is thus the focus of this paper to monitor his/her posture and action via TPA image sensor and then predict that an action of getting off the bed will occur in a near future (e.g., S seconds later). Our system can raise an alarm to the caregivers so that they can intervene and offer the necessary assistance. A deep-learning model based on CNN-RNN (Convolutional neural network-Recurrent neural network) architecture was designed which is capable of predicting the elderly bed-exit intention by S =5.78 seconds in advance of the action onset at an accuracy of 99.37% according to our dataset evaluation. Our system is also suitable for on-line real-time operation which will be helpful to elderly caring in our society.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2022.3229059