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Lifesaver: Android-based Application for Human Emergency Falling State Recognition

  • Abbas, Qaisar (College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU))
  • Received : 2021.08.05
  • Published : 2021.08.30

Abstract

Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.

Keywords

Acknowledgement

The author would like to express their cordial thanks to the deanship of scientific research at Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

References

  1. I. Figueiredo, C. Leal, L. Pinto, J. Bolito and A. Lemos, "Exploring smartphone sensors for fall detection", mUX: The Journal of Mobile User Experience, vol. 5, no. 1, pp. 2-9, 2016 https://doi.org/10.1186/s13678-016-0004-1
  2. Falls in the elderly Causes and prevention-Al Riyadh newspaper [Online]. Available: htp://www.alriyadh.com/882371. [Accessed: 04- Nov2016].
  3. J. Davis, M. Robertson, M. Ashe, T. Liu-Ambrose, K. Khan, and C. Marra, "International comparison of cost of falls in older adults living in the community: a systematic review," Osteoporosis International, vol. 21, no. 8, pp. 1295-1306, 2010. https://doi.org/10.1007/s00198-009-1162-0
  4. E. Stone and M. Skubic, "Fall Detection in Homes of Older Adults Using the Microsoft Kinect", IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 1, pp. 290-301, 2015. https://doi.org/10.1109/JBHI.2014.2312180
  5. S. Sabrina Schnhart, "History of Algorithms", Cs-exhibitions.uni-klu.ac.at, 2016. [Online]. Available: http://cs-exhibitions.uni-klu.ac.at/index.php?id=193. [Accessed: 15- Dec- 2016].
  6. A. Iazzi, R. Thami and M. Rziza, "A novel approach to improve background subtraction method for fall detection system", in 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), 2015.
  7. C. Rougier, J. Meunier, A. St-Arnaud and J. Rousseau, "Robust Video Surveillance for Fall Detection Based on Human Shape Deformation", IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 5, pp. 611-622, 2011. https://doi.org/10.1109/TCSVT.2011.2129370
  8. E. Stone and M. Skubic, "Fall Detection in Homes of Older Adults Using the Microsoft Kinect", IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 1, pp. 290-301, 2015. https://doi.org/10.1109/JBHI.2014.2312180
  9. A. Tang, C. Ong and A. Ahmad, "Fall Detection Sensor System for the Elderly", International Journal of Advanced Computer Research, vol. 5, no. 19, pp. 176-184, 2015
  10. "Arduino UNO", Arduino.org, 2016. [Online]. Available: http://www.arduino.org/products/boards/arduino-uno. [Accessed: 10- Dec- 2016].
  11. "Arduino - ArduinoGSMShield", Arduino.cc, 2016. [Online]. Available: https://www.arduino.cc/en/Main/ArduinoGSMShield. [Accessed: 10- Dec- 2016].
  12. A. Ozdemir and B. Barshan, "Detecting Falls with Wearable Sensors Using Machine Learning Techniques", Sensors, vol. 14, no. 6, pp. 10691-10708, 2014. https://doi.org/10.3390/s140610691
  13. "MTw Development Kit Lite - Products - Xsens 3D motion tracking", Xsens 3D motion tracking, 2016. [Online]. Available: https://www.xsens.com/products/mtwdevelopment-kit-lite/. [Accessed: 11- Dec- 2016].
  14. T. Vilarinho and B. Farshchian, "A Combined Smartphone and Smartwatch Fall Detection System", 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015.
  15. L. Schwickert, C. Becker, U. Lindeman, C. Mar'echal, a. Bourke,L. Chiari, J. L. Helbostad, W. Zijlstra, K. Aminian, C. Todd,S. Bandinelli, and J. Klenk, "Fall detection with body-worn sensors: a systematic review." Zeitschrift fur Gerontology und Geriatrie,vol. 46,no. 8, pp. 706-19, Dec. 2013. https://doi.org/10.1007/s00391-013-0559-8
  16. S. Abbate, M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini and A. Vecchio, "A smartphone-based fall detection system", Pervasive and Mobile Computing, vol. 8, no. 6, pp. 883-899, 2012. https://doi.org/10.1016/j.pmcj.2012.08.003
  17. VO, Quang Viet; LEE, Gueesang; CHOI, Deokjai. Fall detection based on movement and smart phone technology. In: Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012 IEEE RIVF International Conference on. IEEE, 2012. p. 1-4.
  18. Z. Zhao, Y. Chen, S. Wang and Z. Chen, "FallAlarm: Smart Phone Based Fall Detecting and Positioning System", Procedia Computer Science, vol. 10, pp. 617-624, 2012. https://doi.org/10.1016/j.procs.2012.06.079
  19. H. Ketabdar and T. Polzehl, "Fall and emergency detection with mobile phones", in In Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility, 2016, p. 2.
  20. "iOS Programming 101: How To Get the User Location in iPhone App", Appcoda.com, 2017. [Online]. Available: http://www.appcoda.com/how-to-get-current-locationiphone-user/. [Accessed: 14- Sep- 2017].
  21. D. [duplicate], "Dialing a phone number in Xcode", Stackoverflow.com, 2017. [Online]. Available: http://stackoverflow.com/questions/12219484/dialing-aphone-number-in-xcode. [Accessed: 19- Sep- 2017].
  22. D. down, "Detect if the Person falls down", Stackoverflow.com, 2017. [Online]. Available: http://stackoverflow.com/questions/8804227/detect-if-theperson-falls-down. [Accessed: 06- Sep- 2017].
  23. D. bumped, "Detect when an iphone has been bumped", Stackoverflow.com, 2017. [Online]. Available: http://stackoverflow.com/questions/6937867/detect-when-an-iphone-has-been-bumped. [Accessed: 06- Sep- 2017].
  24. J. Hoffman, "iOS Programming Recipe 19: Using Core Motion to Access Gyro and Accelerometer", NSCookbook.com, 2017. [Online]. Available: http://nscookbook.com/2013/03/ios-programming-recipe19-using-core-motion-to-access-gyro-and-accelerometer/. [Accessed: 06- Sep- 2017].
  25. Cdn.intechopen.com, 2017. [Online]. Available: http://cdn.intechopen.com/pdfs-wm/12472.pdf. [Accessed: 08- Sep- 2017].
  26. G. T. Tsenov and V. M. Mladenov, "Speech recognition using neural networks," in 0th Symposium on Neural Network Applications in Electrical Engineering, Belgrade, Serbia, 2010.
  27. W. Zhang, C. Xiaodong , F. Ulrich , K. Brian , S. George , K. David and P. Michael , "Distributed Deep Learning Strategies for Automatic Speech Recognition," in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019.
  28. P. M. Mohamad and F. Fardad, "An Advanced Method for Speech Recognition," in Engineering and Technology:World Academy of Science, 2009.