ABSTRACT
In this paper, we present a novel fall detection method using wearable sensors that are inexpensive and easy to deploy. A new, simple, yet effective feature extraction scheme is proposed, in which features are extracted from slices or quanta of sliding windows on the sensor's continuously acceleration data stream. Extracted features are used with a support vector machine model, which is trained to classify frames of data streams into containing falls or not. The proposed method is rigorously evaluated on a dataset containing 144 falls and other activities of daily living (which produces significant noise for fall detection). Results shows that falls could be detected with 91.9% precision and 94.4% recall. The experiments also demonstrate the superior performance of the proposed methods over three other fall detection methods.
- ADXL330: http://www.analog.com/static/imported-files/data_sheets/ADXL330.pdf (accessed on 5/25/2014)Google Scholar
- Alwan, M., Rajendran, P. J., Kell, S., Mack, D., Dalai, S., Wolfe, M., & Felder, R. (2006, April). A smart and passive floor-vibration based fell detector for elderly. In Information and Communication Technologies, 2006. ICTTA'06. 2nd (Vol. 1, pp. 1003--1007). IEEE.Google Scholar
- Bagalà, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J. M., ... & Klenk, J. (2012). Evaluation of accelerometer-based fall detection algorithms on real-world fells. PloS one, 7(5), e37062.Google ScholarCross Ref
- Balazinska, M., Deshpande, A., Franklin, M. J., Gibbons, P. B., Gray, J., Hansen, M., ... & Tao, V. (2007). Data management in the worldwide sensor web. IEEE Pervasive Computing, 6(2), 30--40. Google ScholarDigital Library
- Bourke, A. K., & Lyons, G. M. (2008). A threshold-based fell-detection algorithm using a bi-axial gyroscope sensor. Medical engineering & physics, 30(1), 84--90.Google Scholar
- Bourke, A. K., O'brien, J. V., & Lyons, G. M. (2007). Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & posture, 26(2), 194--199.Google Scholar
- Brickhouse Alert: http:/www.brickhousealert.comlpersonal-emergency-medical-alarm.html (accessed on 5/25/2013)Google Scholar
- Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press. Google ScholarCross Ref
- Dinh, K. Q., Pham, C., Nguyen, N. D., Tu, M. P. 2013. Real-Time Fall Detection Using an Accelerometer. Journal of Science and Technology 51(1A) 2013 (pp. 108--116) (in Vietnamese)Google Scholar
- Doukas, C., Maglogiannis, I., Tragas, P., Liapis, D., & Yovanof, G. (2007). Patient fall detection using support vector machines. In Artificial Intelligence and Innovations 2007: from Theory to Applications (pp. 147--156). Springer US.Google ScholarCross Ref
- Doukas, C., Maglogiannis, I., Tragas, P., Liapis, D., & Yovanof, G. (2007). Patient fall detection using support vector machines. In Artificial Intelligence and Innovations 2007: from Theory to Applications (pp. 147--156). Springer US.Google ScholarCross Ref
- Fu, Z., Culurciello, E., Lichtsteiner, P., & Delbruck, T. (2008, May). Fall detection using an address-event temporal contrast vision sensor. In Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on (pp. 424--427). IEEE.Google Scholar
- Hazelhoff, L., & Han, J. (2008, January). Video-based fall detection in the home using principal component analysis. In Advanced Concepts for Intelligent Vision Systems (pp. 298--309). Springer Berlin Heidelberg. Google ScholarDigital Library
- Hwang, J. Y., Kang, J. M., Jang, Y. W., & Kim, H. C. (2004, September). Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly. In Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE (Vol. 1, pp. 2204--2207). IEEE.Google Scholar
- Jantaraprim, P., Phukpattaranont, P., Limsakul, C., & Wongkittisuksa, B. (2012). Fall Detection for the elderly using a Support Vector Machine. International Journal of Soft Computing and Engineering, 2(1), 484--490.Google Scholar
- Lai, C. F., Chang, S. Y., Chao, H. C., & Huang, Y. M. (2011). Detection of cognitive injured body region using multiple triaxial accelerometers for elderly falling. Sensors Journal, IEEE, 11(3), 763--770.Google ScholarCross Ref
- Lindemann, U., Hock, A., Stuber, M., Keck, W., & Becker, C. (2005). Evaluation of a fall detector based on accelerometers: A pilot study. Medical and Biological Engineering and Computing, 43(5), 548--551.Google ScholarCross Ref
- Noury, N., Hervé, T., Rialle, V., Virone, G., Mercier, E., Morey, G., ... & Porcheron, T. (2000). Monitoring behavior in home using a smart fall sensor and position sensors. In Microtechnologies in Medicine and Biology, 1st Annual International, Conference On. 2000 (pp. 607--610). IEEE.Google Scholar
- Noury, N., Tarmizi, A., Savall, D. et al. 2003. A smart sensor for fall detection in daily routine. In SICICA2003, Aveiro-Portugal, 9--11 Jul 2003.Google Scholar
- Pham, C., & Olivier, P. (2009). Slice&dice: Recognizing food preparation activities using embedded accelerometers (pp. 34--43). Springer Berlin Heidelberg. Google ScholarDigital Library
- Pham, C., & Phuong, T. M. (2013). Real-time fall detection and activity recognition using low-cost wearable sensors. In Computational Science and Its Applications--ICCSA 2013 (pp. 673--682). Springer Berlin Heidelberg. Google ScholarDigital Library
- Reece, A. C., & Simpson, J. M. (1996). Preparing older people to cope after a fell. Physiotherapy, 82(4), 227--235.Google ScholarCross Ref
- Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2011). Robust video surveillance for fall detection based on human shape deformation. Circuits and Systems for Video Technology, IEEE Transactions on, 21(5), 611--622. Google ScholarDigital Library
- Stevens, J. A., & Dellinger, A. M. (2002). Motor vehicle and fall related deaths among older Americans 1990--98: sex, race, and ethnic disparities. Injury Prevention, 8(4), 272--275.Google ScholarCross Ref
- Wii Remote: http://en.wikipedia.org/wiki/Wii_Remote (accessed on 5/25/2014)Google Scholar
- Wild, D., Nayak, U. S., & Isaacs, B. (1981). How dangerous are falls in old people at home? British medical journal (Clinical research ed.), 282(6260), 266.Google Scholar
- Wu, G. E., & Xue, S. (2008). Portable preimpact fall detector with inertial sensors. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 16(2), 178--183.Google Scholar
- Wu, W., Au, L., Jordan, B., Stathopoulos, T., Batalin, M., Kaiser, W., ... & Chodosh, J. (2008, March). The SmartCane system: an assistive device for geriatrics. In Proceedings of the ICST 3rd international conference on Body area networks (p. 2). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). Google ScholarDigital Library
- Yu, X. (2008, July). Approaches and principles of fall detection for elderly and patient. In e-health Networking, Applications and Services, 2008. HealthCom 2008. 10th International Conference on (pp. 42--47). IEEE.Google Scholar
- Zhang, T., Wang, J., Xu, L., & Liu, P. (2006). Fall detection by wearable sensor and one-class SVM algorithm. In Intelligent Computing in Signal Processing and Pattern Recognition (pp. 858--863). Springer Berlin Heidelberg.Google ScholarCross Ref
- Zhang, T., Wang, J., Xu, L., & Liu, P. (2006). Using wearable sensor and NMF algorithm to realize ambulatory fall detection. In Advances in Natural Computation (pp. 488--491). Springer Berlin Heidelberg. Google ScholarDigital Library
- Zigel, Y., Litvak, D., & Gannot, I. (2009). A method for automatic fall detection of elderly people using floor vibrations and sound---Proof of concept on human mimicking doll falls. Biomedical Engineering, IEEE Transactions on, 56(12), 2858--2867.Google ScholarCross Ref
Index Terms
- A classifier based approach to real-time fall detection using low-cost wearable sensors
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