Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS)

Yi  He; Ye  Li; Chuan  Yin

doi:10.4236/etsn.2012.11001

Yi He, Ye Li^*, Chuan Yin

Abstract: This paper presents a system with real-time classification of human movements based on smartphone mounted on the waist. The built-in tri-accelerometer was utilized to collect the information of body motion. At the same time, the smartphone is able to classify the data for activity recognition. By our algorithm, body motion can be classified into five different patterns: vertical activity, lying, sitting or static standing, horizontal activity and fall. It alarms falling by Multimedia Messaging Service (MMS) with map of suspected fall location, GPS coordinate and time etc. If a fall was suspected, an automatic MMS would be sent to preset people. The major advantage of the proposed system is the novel application of smartphone which already have the necessary sensors and can monitor fall ubiquitously without any additional devices.

Keywords: Multimedia Messaging Service (MMS); Smartphone; Tri-Accelerometer; Falling-Incident Detection

Cite this paper: Y. He, Y. Li and C. Yin, "Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS)," E-Health Telecommunication Systems and Networks, Vol. 1 No. 1, 2012, pp. 1-5. doi: 10.4236/etsn.2012.11001.

References

[1] J. M. Hausdorff, D. A. Rios, and H. K. Edelberg, "Gait variability and fall risk in community-living older adults: A 1-year prospective study," Archives of Physical Medicine and Rehabilitation, vol. 82, pp. 1050-1056, 2001.

[2] W. H. O. Ageing and L. C. Unit, WHO global report on falls prevention in older age: World Health Organization, 2008.

[3] S. R. Lord, C. Sherrington, and H. B. Menz, Falls in older people: risk factors and strategies for prevention. Cambridge: Cambridge Univ Pr, 2007.

[4] B. Schulze, M. Floeck, and L. Litz, "Concept and Design of a Video Monitoring System for Activity Recognition and Fall Detection," Ambient Assistive Health and Wellness Management in the Heart of the City, pp. 182-189, 2009.

[5] A. Sixsmith and N. Johnson, "A smart sensor to detect the falls of the elderly," IEEE Pervasive Computing, pp. 42-47, 2004.

[6] M. Yu, S. M. Naqvi, A. Rhuma, and J. Chambers, "Fall Detection in a Smart Room by Using a Fuzzy One Class Support Vector Machine and Imperfect Training Data," in Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 1833-1836.

[7] J. Ben-Arie, Z. Wang, P. Pandit, and S. Rajaram, "Human activity recognition using multidimensional indexing," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1091-1104, 2002.

[8] D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, "Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring," Information Technology in Biomedicine, IEEE Transactions on, vol. 10, pp. 156-167, 2006.

[9] T. Choudhury, G. Borriello, S. Consolvo, D. Haehnel, B. Harrison, B. Hemingway, and J. Hightower, "The mobile sensing platform: An embedded activity recognition system," IEEE Pervasive Computing, pp. 32-41, 2008.

[10] H. Ghasemzadeh, R. Jafari, and B. Prabhakaran, "A body sensor network with electromyogram and inertial sensors: multimodal interpretation of muscular activities," Information Technology in Biomedicine, IEEE Transactions on, vol. 14, pp. 198-206, 2010.

[11] I. Maglogiannis and C. Doukas, "Emergency Fall Incidents Detection in Assisted Living Environments Utilizing Motion, Sound and Visual Perceptual Components," Information Technology in Biomedicine, IEEE Transactions on, pp. 1-1, 2011.

[12] E. M. Tapia, S. S. Intille, W. Haskell, K. Larson, J. Wright, A. King, and R. Friedman, "Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor," in Wearable Computers, 2007 11th IEEE International Symposium on, 2007, pp. 37-40.

[13] R. Y. W. Lee and A. J. Carlisle, "Detection of falls using accelerometers and mobile phone technology," Age and Ageing, pp. 1-7, 2011.

[14] T. Zhang, J. Wang, P. Liu, and J. Hou, "Fall detection by embedding an accelerometer in cellphone and using KFD algorithm," IJCSNS International Journal of Computer Science and Network Security, vol. 6, pp. 277-284, 3 2006.

[15] J. Yang, "Toward physical activity diary: motion recognition using simple acceleration features with mobile phones," in IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics 2009, pp. 1-10.

[16] T. Saponas, J. Lester, J. Froehlich, J. Fogarty, and J. Landay, "ilearn on the iphone: Real-time human activity classification on commodity mobile phones," University of Washington CSE Tech Report UW-CSE-08-04-02, 2008.

[17] M. Kangas, A. Konttila, I. Winblad, and T. Jamsa, "Determination of simple thresholds for accelerometry-based parameters for fall detection," in Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, 2007, pp. 1367-1370.

[18] C. V. C. Bouten, K. T. M. Koekkoek, M. Verduin, R. Kodde, and J. D. Janssen, "A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity," Biomedical Engineering, IEEE Transactions on, vol. 44, pp. 136-147, 1997.