Real-Time Action Recognition System for Elderly People Using Stereo Depth Camera

Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the eld...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-09, Vol.21 (17), p.5895
Main Authors: Zin, Thi Thi, Htet, Ye, Akagi, Yuya, Tamura, Hiroki, Kondo, Kazuhiro, Araki, Sanae, Chosa, Etsuo
Format: Article
Language:English
Subjects:
Accuracy
ambient assisted living
Cameras
Caregivers
Computer vision
depth map features
depth motion appearance
depth motion history
Elder care
Health care
Histograms
Human influences
Internet of Things
Machine learning
Monitoring systems
Older people
Privacy
Real time
Researchers
Rounding
Sensors
Sequences
stereo depth camera
UV-disparity maps
Wireless networks
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Summary:Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we introduce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21175895