Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : G S Akshay | R Anush | M Kumaresan | T R Lekhaaunnamed

Volume 04 Issue 04 2017

ISSN no :  2348-3121

Page no: 84-85

Abstract – In this paper the detecting the accident by falling and corresponding rescue system in wide area network based on a smart phone and the third generation (3G) networks. To realize the detection of falling position algorithm, the angles acquired by the electronic compass, axis which itself free to alter in direction by gyroscope and the waveform sequence of the tri-axial accelerometer on the smart phone are used as the system inputs. The signals that acquired are used to generate and examined an ordered in a sequential manner by the proposed cascade classifier and gyroscope for detection purpose. And the Gravity clustering algorithm which computes the human body behavior patterns according to the relationship between the center of gravity in the body and the feet portion of the body. Once event is detected by accident fall, the position of  user’s can be accumulate  by the global positioning system (GPS), gyroscope and sent the message to the  rescue center and get the medical help immediately through the 3G communication network. 


  1. M. Sekine, T. Tamura, M. Akay, T. Fujimoto, T.Togawa, and Y. Fukui, “Discrimination of walking patterns using wavelet-based fractal analysis,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 10, no. 3, pp. 188–196, Sep.2002.
  2. G.Wu and S. Xue, “Portable preimpact fall detector with inertial sensors,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 16, no. 2, pp. 178–183, Apr. 2008.
  3. Y. Zigel, D. Litvak, and I. Gannot, “A method for automatic fall detection of elderly people using floor vibrations and sound-proof of concept on human mimicking doll falls,” IEEE Trans. Biomed. Eng., vol. 56, no. 12, pp. 2858–2867, Dec. 2009.
  4. C.-F. Lai, Y.-M. Huang, J. H. Park, and H.-C. Chao, “Adaptive body posture analysis for elderly-falling detection with multisensors,” IEEE Intell. Syst., vol. 25, no. 2, pp. 20–30, Mar./Apr. 2010.
  5. F. Bianchi, S.-J. Redmond, M.-R. Narayanan, S. Cerutti, and N.-H. Lovell, “Barometric pressure and triaxial accelerometry-based falls event detection,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 6, pp. 619–627, Dec. 2010.
  6. H. Rimminen, J. Lindstr¨om, M. Linnavuo, and R. Sepponen, “Detection of falls among the elderly by a floor sensor using the electric near field,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 6, pp. 1475–1476, Nov.2010.
  7. E.Auvinet, F. Multon, A. Saint-Arnaud, J. Rousseau, and J. Meunier, “Fall detection with multiple cameras: An occlusion-resistant method based on 3-D silhouette vertical distribution,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 2, pp. 290–300, Mar. 2011.
  8. T. Shany, S. J. Redmond, M. R. Narayanan, and N. H. Lovell, “Sensors-Based wearable systems for monitoring of human movement and falls,” IEEE Sensors J., vol. 12, no. 3, pp. 658–670, Mar. 2012.
  9. J. Cheng, X. Chen, and M. Shen, “A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals,” IEEE J. Biomed. Health Informatics, vol. 17, no. 1, pp. 38–45, Jan. 2013.
  10. L. Tong, Q. Song, Y. Ge, and M. Liu, “HMM-Based human fall detection and prediction method using tri-axial accelerometer,” IEEE Sensors J., vol. 13, no. 5, pp. 1849–1856, May 2013.