Abstract
Fall detection has been an active research problem as fall detection technology is critical for the ageing-at-home of the elderly and it can enhance life safety of the elderly and boost their confidence of ageing-at-home by immediately alerting fall occurrence to care givers. This paper presents an algorithm of fall detection for the ageing-at-home of the elderly. This algorithm detects fall events by identifying (human) shape state change pattern reflecting a fall incident from video recorded by a single fixed camera. The novelty of the algorithm is multiple. First, it detects fall occurrence by identifying the state change pattern. Second, it uses the camera projection matrix in its computing. Thus, it eliminates camera setting-related learning. Lastly, it adds constraints to state change pattern to reduce false alarms. Experiments show that the proposed algorithm has a promising performance.
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References
Anderson, D., Keller, J.M., Skubic, M., Chen, X., He, Z.: Recognizing falls from silhouettes. In: EMBS 2006 (28th Int’l Conf. of IEEE Eng. in Medicine and Biology Society), August 2006, pp. 6388–6391 (2006)
Cucchiara, R., Prati, A., Vezzani, R.: A multi-camera vision system for fall detection and alarm generation. Expert Systems Journal 24(5), 334–345 (2007)
Hsu, Y.T., Hsieh, J.W., Kao, H.F., Liao, H.Y.M.: Human behavior analysis using deformable triangulations. In: IEEE 7th Workshop on MM Signal Processing, October 2005, pp. 1–4 (2005)
Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Pervasive Health Conference and Workshops 2006, November 29-December 1, pp. 1–4 (2006)
Miaou, S.G., Shih, F.C., Huang, C.Y.: A smart vision-based human fall detection system for telehealth applications. In: 3rd IASTED Int’l Conf. on Telehealth, Montreal, Quebec, Canada, May 30-June 1, p. 564 (2007)
Nart-Charif, H., McKenna, S.J.: Activity summarisation and fall detection in a supportive home environment. In: ICPR 2004 (2004)
Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall detection from human shape and motion history using video surveillance. In: 21st Int’l Conf. on Advanced Information Networking & Applications Workshops, 2007, AINAW 2007, vol. 2, pp. 875–880 (2007)
Thome, N., Miguet, S.: A HHMM-Based approach for robust fall detection. In: ICARCV 2006 (9th Int’l Conf. on Control, Automation, Robotics and Vision), December 5-8, pp. 1–8 (2006)
Töreyin, B.U., Dedeoğlu, Y., Çetin, A.E.: HMM based falling person detection using both audio and video. In: IEEE 14th Signal Processing & Com. Applications, April 17-19 (2006)
Yu, X.: Approaches and principles of fall detection for elderly and patient. In: Healthcom 2008, Singapore, July 7-9 (2008)
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Yu, X., Wang, X., Kittipanya-Ngam, P., Eng, H.L., Cheong, LF. (2009). Fall Detection and Alert for Ageing-at-Home of Elderly. In: Mokhtari, M., Khalil, I., Bauchet, J., Zhang, D., Nugent, C. (eds) Ambient Assistive Health and Wellness Management in the Heart of the City. ICOST 2009. Lecture Notes in Computer Science, vol 5597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02868-7_26
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DOI: https://doi.org/10.1007/978-3-642-02868-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02867-0
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