DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK

International Journal of Computational Intelligence Systems

Volumn 6, Issue 1, 2013, Pages 127-136

  Kerdegari, Hamideh ^a   Samsudin, Khairulmizam ^a   Rahman Ramli, Abdul ^a   Mokaram, Saeid ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Classification; Multilayer perceptron; Tri axial accelerometer; Wearable fall detection system

Indexed keywords

ACCELERATION PATTERN; ACTIVITIES OF DAILY LIVING; FALL DETECTION; HUMAN BODIES; HUMAN FALL DETECTION; MULTI LAYER PERCEPTRON; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; PHYSICAL CHARACTERISTICS; TRIAXIAL ACCELEROMETER;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTER SCIENCE;

ACCELEROMETERS;

EID: 84872395026     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2013.761769     Document Type: Article

References (20)

1. Tinetti, M.E. and Williams, C.S. 1997. Falls, injuries due to falls, and the risk of admission to a nursing home. New England Journal of Medicine, 337(18): 1279-84.
2. Bourke Alan K. van de Ven Pepijn W.J. Chaya Amy E; Olaighin Gearoid M. Nelson John, The design and development of a long term fall detection system incorporated into a custom vest for the elderly., Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 2836-2839, 20-25 Aug 2008.
3. Xinguo Yu, Approaches and principles of fall detection for elderly and patient, e-health Networking, Applications and Services, 2008. HealthCom 2008. 10th International Conference on, pp. 42-47, 7-9 July 2008
4. Hijaz, F.; Afzal, N.; Ahmad, T.; Hasan, O., Survey of fall detection and daily activity monitoring techniques, Information and Emerging Technologies (ICIET), 2010 International Conference on, pp. 1-6, 14-16 June 2010
5. Kangas, M.; Konttila, A.; Winblad, I.; Jamsa, T., Determination of simple thresholds for accelerometry-based parameters for fall detection, Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pp. 1367-1370, 22-26 Aug. 2007
6. Bourke, A.K, O'Brien, J.V and Lyons, G.M. 2007. Evaluation of a threshold based triaxial accelerometer fall detection algorithm. Gait Posture, 26(2): 194-199.
7. Jiewen Zheng Guang Zhang Taihu Wu, Design of Automatic Fall Detector for Elderly Based on Triaxial Accelerometer, Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on, pp. 1-4, 11-13 June 2009
8. Zhang T, Wang J, Xu L and Liu P Using wearable sensor and NMF algorithm to realize ambulatory fall detection. Advances in Natural Computation: Part 2 (Lecture Notes in Computer Science vol 4222). Berlin: Springer pp 488-91.
9. Zhang, T, Wang, J, Xu, L and Liu, P. 2006. Fall detection by wearable sensor and one-class SVM algorithm. Engineering Intelligent Computing in Signal Processing and Pattern Recognition, Lecture Notes in Control and Information Sciences, 345: 858-863.
10. ADXL345: http://www.analog.com/
11. ADXL345Evaluation Board: http://www.sparkfun.com/
12. Qiang Li Stankovic, J.A.; Hanson, M.A.; Barth, A.T.; Lach, J. Gang Zhou, Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information, Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on, pp. 138-143, 3-5 June 2009
13. Hwang, J.Y.; Kang, J.M.; Jang, Y.W.; Kim, H.C.; Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly, Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE, pp. 2204-2207, 1-5 Sept. 2004
14. Torrent M.; Bourke A.; Parra X.; Català A. Ambulatory Mobility Characterization Using Body Inertial Systems: An Application to Fall Detection, in Proc. IWANN (1), 2009, pp. 1129-1136.
15. Preece, S. 2009. Activity identification using body-mounted sensors-a review of classification techniques. Physiological Measurement, 30(4) April: R1-R33.
16. Estudillo-Valderrama, M.A., Roa, L.M., Reina-Tosina, J. and Naranjo-Hernandez, D. 2009. Design and Implementation of a Distributed Fall Detection System-Personal Server. Information Technology in Biomedicine, IEEE Transactions on, 13(6) Nov: 874-881.
17. Hsu-Yang Kung Chin-Yu Ou Shin-Di Li Chun-Hao Lin Hong-Jie Chen Yu-Lun Hsu Miao-Han Chang Che-I Wu, Efficient movement detection for human actions using triaxial accelerometer, Consumer Electronics (ICCE), 2010 Digest of Technical Papers International Conference on, pp. 113-114, 9-13 Jan. 2010
18. Tom M. Mitchell. Machine learning. USA The McGraw-Hill Science, 1997, pp. 81-126.
19. Hamideh Kerdegari Khairulmizam Samsudin; Abd Rahman Ramli Saeid Mokaram, Evaluation of Fall Detection Classification Approaches, presented at the International Conference on Intelligent and Advanced Systems (ICIAS), Malaysia, 12-14 June 2012
20. Noury, N.; Fleury, A.; Rumeau, P.; Bourke, A.K.; Laighin, G.O.; Rialle, V.; Lundy, J.E., Fall detection - Principles and Methods, Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pp. 1663-1666, 22 - Aug. 2007