Dangerous Situation Detection for Elderly Persons in Restrooms Using Center of Gravity and Ellipse Detection
Lin Meng*, Xiangbo Kong*, and Daiki Taniguchi**
*College of Science and Engineering, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan
**Grand Electronics, Inc.
754 Ayanishinotouin-cho, Shimogyou-ku, Kyoto-shi, Kyoto 600-8474, Japan
We developed a restroom danger detection system (RDDS) for detecting dangerous situations and protecting the elderly. Restrooms are particularly dangerous places for elderly persons. Our RDDS detects danger in real time by using image processing and sends an alert to a family member, hospital staff, etc. It comprises four processes: person detection, center of gravity detection, ellipse detection, and danger decision. The human detection process calculates the difference between an image of the empty restroom and one of the restroom when it is occupied (to which a brightness correction has been applied). The difference image is binarized and used for detecting the presence of a person. If a person is detected, the person’s center of gravity and ellipse are detected in the binarized image after it is denoised. The obtained information is used for detecting a dangerous situation. If the dangerous situation continues for 60 seconds, an alert is sent. Testing showed that our system can detect a dangerous situation within 1.5 seconds. This RDDS is one step toward the development of a comprehensive elderly person protection system.
-  N. Noury, A. Fleury, P. Rumeau, A. K. Bourke, G. O. Laighin, V. Rialle, and J. E. Lundy, “Fall detection – principles and methods,” 29th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), pp. 1663-1666, 2007.
-  N. Noury, P. Rumeau, A. K. Bourke, G. O. Laighin, and J. E. Lundy, “A proposal for the classification and evaluation of fall detectors,” IRBM, Vol.29, No.6, pp. 340-349, 2008.
-  J. Tao, M. Turjo, M. F. Wong, M. Wang, and Y. P. Tan, “Fall incidents detection for intelligent video surveillance,” Fifth Int. Conf. on Information, Communications and Signal Processing, pp. 1590-1594, 2005.
-  B. Toreyin, Y. Dedeoglu, and A. Cetin, “Hmm based falling person detection using both audio and video,” Proc. IEEE Int. Workshop on Human-Computer Interaction, pp. 211-220, 2005.
-  D. Anderson, J. M. Keller, M. Skubic, X. Chen, and Z. He, “Recognizing falls from silhouettes,” Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 6388-6391, 2006.
-  T. Lee and A. Mihailidis, “An intelligent emergency response system: preliminary development and testing of automated fall detection,” J. of Telemedicine and Telecare, Vol.11, No.4, pp. 194-198, 2005.
-  A. Sixsmith and N. Johnson, “A smart sensor to detect the falls of the elderly,” IEEE Pervasive Computing, Vol.3, No.2, pp. 42-47, 2004.
-  C. Wu and H. Aghajan, “Head pose and trajectory recovery in uncalibrated camera networks – region of interest tracking in smart home applications,” ACM/IEEE Int. Conf. on Distributed Smart Cameras, pp. 1-7, 2008.
-  H. Nait-Charif and S. J. McKenna, “Activity summarisation and fall detection in a supportive home environment,” Proc. of the 17th Int. Conf. on Pattern Recognition (ICPR), Vol.4, pp. 323-326, 2004.
-  D. Anderson, R. H. Luke, J. M. Keller, M. Skubic, M. Rantz, and M. Aud, “Linguistic summarization of video for fall detection using voxel person and fuzzy logic,” Computer Vision and Image Understanding, Vol.113, No.1, pp. 80-89, 2009.
-  C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, “Robust Video Surveillance for Fall Detection Based on Human Shape Deformation,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.21, No.5, pp. 611-622, 2011.
-  E. Auvinet, L. Reveret, A. St-Arnaud, J. Rousseau, and J. Meunier, “Fall detection using multiple cameras,” 30th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 2554-2557, 2008.
-  L. Hazelhoff, J. Han, and P. H. N. With, “Video-based fall detection in the home using principal component analysis,” ACIVS ’08 Proc. of the 10th Int. Conf. on Advanced Concepts for Intelligent Vision Systems, Vol.1, pp. 298-309, 2008.
-  Y. Mori and S. Kido, “Monitoring System for Elderly People Using Passive RFID Tags,” J. Robot. Mechatron., Vol.26, No.5, pp. 649-655, 2014.
-  N. Thome, S. Miguet, and S. Ambellouis, “A real-time, multiview fall detection system: A LHMM-based approach,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.18, No.11, pp. 1522-1532, 2008.
-  D. Taniguchi, M. Taniguchi, Z. K. Wang, and L. Meng, “A Toilet Danger Detection System for Aged People,” Proc. of the Int. Electrical Engineering Congress, 2014.
-  L. Meng, X. B. Kong, and D. Taniguchi, “Danger situations detection for the senior in toilet room using the center of gravity,” 2016 Int. Conf. on Advanced Mechatronic Systems (ICAMechS), 2016.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.