Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System
Abstract
:1. Introduction
- Develop a monitoring system that provides a visual understanding of a person’s situation and can judge whether the state is abnormal or normal based on video data acquired using a simple and affordable RGB camera;
- Develop an individualized and modified statistical analysis on each of the extracted features, providing trustworthy information, not only on the definite moment of a fall, but also on the period of a fall;
- Develop an efficient way of using a Hidden Markov Model (HMM) for the detailed detection of sequential abnormal and normal states for the person being monitored.
2. Related Works
3. Proposed Architecture of the System
3.1. Object Detection
3.2. Feature Extraction
3.3. Analysis of Abnormal and Normal Events
3.4. Decision-Making Rules
4. Experiments
4.1. The Dataset
4.2. Experimental Results
4.3. Performance Evaluation of the Proposed Approach
- Detected Abnormal State (As1): A video frame represents an abnormal state, and is correctly classified as “Positive Abnormal”;
- Undetected Abnormal State (As2): A video frame represents an abnormal state, and is incorrectly classified as “Negative Normal”;
- Normal State (Ns1): A video frame does not represent an abnormal state, and is correctly classified as “Negative Normal”;
- Mis-detected Normal State (Ns2): A video frame does not represent an abnormal state, and is incorrectly classified as “Positive Abnormal”.
4.4. Comparative Studies of the Merits and Demerits of Our Proposed System
5. Discussion and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Videos | Precision (%) | Recall (%) | Accuracy (%) | Specificity (%) | NPV (%) |
---|---|---|---|---|---|
1 | 100 | 100 | 100 | 96.88 | 96.88 |
2 | 100 | 100 | 100 | 96.07 | 96.09 |
3 | 100 | 86.66 | 98.32 | 99.04 | 97.17 |
4 | 100 | 100 | 100 | 97.07 | 97.06 |
5 | 100 | 100 | 100 | 90.40 | 90.40 |
6 | 82.98 | 97.50 | 99.76 | 97.89 | 97.63 |
7 | 98.04 | 100 | 100 | 99.36 | 99.36 |
8 | 100 | 100 | 100 | 94.70 | 94.70 |
9 | 100 | 83.33 | 97.41 | 90.80 | 99.09 |
10 | 100 | 100 | 100 | 97.72 | 97.72 |
11 | 100 | 100 | 100 | 98.54 | 98.54 |
12 | 100 | 100 | 100 | 96.38 | 96.38 |
13 | 100 | 100 | 100 | 88.48 | 88.48 |
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Htun, S.N.N.; Zin, T.T.; Tin, P. Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System. J. Imaging 2020, 6, 49. https://doi.org/10.3390/jimaging6060049
Htun SNN, Zin TT, Tin P. Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System. Journal of Imaging. 2020; 6(6):49. https://doi.org/10.3390/jimaging6060049
Chicago/Turabian StyleHtun, Swe Nwe Nwe, Thi Thi Zin, and Pyke Tin. 2020. "Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System" Journal of Imaging 6, no. 6: 49. https://doi.org/10.3390/jimaging6060049