Comparison of Machine Learning Methods for the Purpose Of Human Fall Detection

1. Eurostat: Statistic data Eurostat. http://epp.eurostat.ec.europa.eu, 2008-09-09.Search in Google Scholar

2. KIM, K. J., ASHTON-MILLER, J. A. 2003. Biomechanics of fall arrest using the upper extremity: age differences. Clin. Biomech. 18(4), pp. 311-318.Search in Google Scholar

3. DONALD, I., BULPITT, C. 1999. The prognosis of falls in elderly people living at home. Age Ageing, 28(2), 121-5.Search in Google Scholar

4. DEANDREA, S., LUCENTEFORTE, E., BRAVI, F., FOSCHI, R., LA VECCHIA, C., NEGRI, E. 2010. Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology, 21(5), pp. 658-668.10.1097/EDE.0b013e3181e8990520585256Search in Google Scholar

5. ALWAN M., RAJENDRAN P.J., KELL S., MACK D., DALAL S., WOLFE M., FELDE R. 2006. A smart and passive floor-vibration based fall detector for elderly. In: Proceedings of ICTTA '06: Information and Communication Technologies. Damascus, pp. 1003-1007.Search in Google Scholar

6. BOURKE, A. K., SCANAILL, C. N., CULHANE, K. M., O'BRIEN, J. V., and LYONS, G. M. 2006. An optimum accelerometer configuration and simple algorithm for accurately detecting falls. In Proceedings of the 24th IASTED international Conference on Biomedical Engineering, pp. 156-160.Search in Google Scholar

7. KANGAS, M., KONTTILA, A., LINDGREN, P., WINBLAD, P., AND JAMSA, T. 2008. Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture, 28(2), pp. 285-291.Search in Google Scholar

8. DOUGHTY, K., LEWIS, R., McINTOSH, A. 2000. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare, Vol. 6, pp. 150-154.Search in Google Scholar

9. NOURY, N., BARRALON, P., VIRONE, G., BOISSY, P., HAMEL. M., RUMEAU, P. 2003. A smart sensor based on rules and its evaluation in daily routines. In Proceedings of the 25th Annual International Conference of the IEEE, Engineering in Medicine Conference of the IEEE, Engineering in Medicine and Biology Society, Vol. 4, pp. 3286-3289.Search in Google Scholar

10. NYAN, M.N, TAY, F.E.H. 2008. Application of motion analysis system in pre-impact fall detection, 9 Engineering Drive 1, Singapore 117576.Search in Google Scholar

11. OLIVIERI, D., N., CONDE, I.G. 2012. Eigenspace-based fall detection and activity recognition from motion templates and machine learning University of Vigo, Spain: Computer Science and Engineering.10.1016/j.eswa.2011.11.109Search in Google Scholar

12. OLIVIERI, D., N., CONDE, I.G. 2012. Eigenspace-based fall detection and activity recognition from motion templates and machine learning. University of Vigo, Spain, Computer Science and Engineering.10.1016/j.eswa.2011.11.109Search in Google Scholar

13. HAN S., LEE S. 2013. A vision-based motion capture and recognition framework for behavior-based safety management. University of Illinois at Urbana-Champaign and University of Michigan.10.1016/j.autcon.2013.05.001Search in Google Scholar

14. STRÉMY, M., PETERKOVÁ, A. 2014. Proposed system for human fall detection using Kinect sensor. In: International Doctoral Seminar 2014. Zielona Góra.Search in Google Scholar

15. CORTES, C., VAPNIK, V. 1995. Support-vector networks. Machine Learning, 20(3): 273.1 0.1007/BF00994018.Search in Google Scholar

16. RUSSELL Stuart, NORVIG Peter. 2003. (1995). Artificial Intelligence: A Modern Approach (2nd ed.). Prentice Hall. ISBN 978-0137903955.Search in Google Scholar

17. WEISSTEIN, E.W. Point-Plane Distance. From MathWorld-A Wolfram Web. Search in Google Scholar