Advertisement

Wearable Sensor-Based Human Fall Detection Wireless System

  • Vaishna S. Kumar
  • Kavan Gangadhar Acharya
  • B. Sandeep
  • T. Jayavignesh
  • Ashvini Chaturvedi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 493)

Abstract

Background/Objectives: Human fall detection is a critical challenge in the healthcare domain since the late medical salvage will even lead to death situations, therefore it requires timely rescue. This research work proposes a system which uses a wearable device that senses human fall and wirelessly raises alerts. Methods/statistical analysis: The detection system consists of the sensor system which contains both accelerometer and gyroscope sensors. The proper orientation of the subject is provided by the Madgwick filter. Six volunteers were engaged to perform the falling and non-falling events. The system is validated and checked by four algorithms: threshold based, support vector machine (SVM), K-nearest neighbor, and dynamic time wrapping, and thus, the accuracy was calculated. Findings: From the results obtained, the SVM has given an accuracy of 93%. Conclusions: When a fall is being detected, an additional feature to check whether the person is in critical state and is lying down for more than a particular time is incorporated and a critical alert is sent to the caretaker’s mobile.

Keywords

Accelerometer Dynamic time wrapping Gyroscope K-nearest neighbor Madgwick filter Support vector machine 

Notes

Acknowledgements

This work was supported by Robert Bosch Engineering and Business Solutions Private Limited, Bangalore. The authors would like to thank the department colleagues, faculty, and friends who supported the work.

References

  1. 1.
    O’Neill T, Varlow J, Silman A, Reeve J, Reid D, Todd C, Woolf A (1994) Age and sex influences on fall characteristics. Ann Rheum Dis, 773–775Google Scholar
  2. 2.
    Kannus P, Sievanen H, Palvanen M, Jarvinen T, Parkkari J (2005) Prevention of falls and consequent injuries in elderly people. Lancet, 1885–1893Google Scholar
  3. 3.
    Holmberg AH, Johnell O, Nilsson PM, Nilsson J, Berglund G, Akesson K (2006) Risk factors for fragility fracture in middle age, a prospective population-based study of 33,000 men and women. Osteoporos, 1065–1077Google Scholar
  4. 4.
    Huynh QT, Nguyen UD, Tran SV, Nabili A, Tran BQ (2013) Fall detection system using combination accelerometer and gyroscope. In: International conference on advances in electronic devices and circuits, pp 52–56Google Scholar
  5. 5.
    Ariani A, Redmond SJ, Chang D, Lovell NH (2010) Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors. In: 32nd annual international conference of the IEEE (EMBS), pp 2115–2118Google Scholar
  6. 6.
    Wang Y, Bai X-Y (2013) Research of fall detection and alarm applications for the elderly. In: International conference on mechatronic sciences, electric engineering and computer (MEC), pp 615–619Google Scholar
  7. 7.
    Zhang Z, Kapoor U, Narayanan M, Lovell NH, Redmond SJ (2011) Design of an unobtrusive wireless sensor network for night time falls detection. In: 33rd annual international conference of the IEEE EMBS, pp 5275–5278Google Scholar
  8. 8.
    Sudarshan BG, Hegde R, Prasanna Kumar SC, Satyanarayana BS (2013) Design and development of fall detector using fall acceleration. IJRET: Int J Res Eng Technol 2321–7308Google Scholar
  9. 9.
    Kailas A (2012) Basic human motion tracking using a pair of gyro and accelerometer MEMS devices. In: IEEE 14th international conference on e-health networking, applications and services instrumentation and measurement, pp 298–302Google Scholar
  10. 10.
    Pierleoni P, Belli A, Palma L, Pellegrini M, Pernini L, Valenti S (2015) A high reliability wearable device for elderly fall detection. IEEE Sens J, 1530–1539Google Scholar
  11. 11.
    Alam F, ZhaiHe Z, Jia H (2015) A comparative analysis of orientation estimation filters using MEMS based IMU. In: 2nd international conference on adaptive science & technology, pp 86–91Google Scholar
  12. 12.
    Wu F, Zhao H, Zhao Y, Zhong H (2015) Development of a wearable-sensor-based fall detection system. Int J Telemed Appl, 576364–576374Google Scholar
  13. 13.
    Tong L, Song Q, Ge Y, Liu M (2013) HMM-based human fall detection and prediction method using tri-axial accelerometer. IEEE Sens J, 1849–1856Google Scholar
  14. 14.
    Honglun H, Meimei H, Minghui W (2013) Sensor-based wireless wearable systems for healthcare and falls monitoring. Int J, 2200–2216Google Scholar
  15. 15.
    Özdemir AT, Barshan B (2014) Detecting falls with wearable sensors using machine learning techniques. Sens J, 10691–10708Google Scholar
  16. 16.
    Dinh A, Teng D, Chen L, Ko SB, Shi Y, Basran J, Del Bello-Hass V (2008) Data acquisition system using six degree-of-freedom inertia sensor and ZigBee wireless link for fall detection and prevention. In: 30th annual international IEEE EMBS conference, pp 2353–2356Google Scholar
  17. 17.
    Mendulkar A, Kale R, Agrawal A (2014) A survey on efficient human fall detection system. Int J Sci Technol Res, 2277–8616Google Scholar
  18. 18.
    Yin X, Shen W, Sarnarabandu J, Wang X (2015) Human activity detection based on multiple smart phone sensors and machine learning algorithms. In: IEEE 19th international conference on computer supported cooperative work in design (CSCWD), pp 582–587Google Scholar
  19. 19.
    Liu S-H, Cheng W-C (2012) Fall detection with the support vector machine during scripted and continuous unscripted activities. Sens J, 12301–12316Google Scholar
  20. 20.
    Jantaraprim P, Phukpattaranont P, Limsakul C, Wongkittisuksa B (2012) Fall detection for the elderly using a support vector machine. Int J Soft Comput Eng (IJSCE), 484–490Google Scholar
  21. 21.
    Liu CL, Lee CH, Lin P-M (2010) A fall detection system using K-nearest neighbor classifier (Elsevier), pp 7174–7181Google Scholar
  22. 22.
    Shi G, Chan CS, Li WJ, Leung K-S, Zou Y, Jin Y (2009) Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier. IEEE Sens J, 495–503Google Scholar
  23. 23.
    Chen J, Kwong K, Chang D, Luk J, Bajcsy R (2005) Wearable sensors for reliable fall detection. In: 27th IEEE annual conference on engineering in medicine and biology, pp 3551–3554Google Scholar
  24. 24.
    Sengto A, Leauhatong T (2012) Human falling detection algorithm using back propagation neural network. In: Biomedical engineering international conference (BMEiCON), pp 978–982Google Scholar
  25. 25.
    Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. Department of Computer Science, National Taiwan University, Taipei 106, Taiwan, pp 1–16Google Scholar
  26. 26.
    Sree Madhubala J, Umamakeswari A (2015) A vision based fall detection system for elderly people. Ind J Sci Technol 8(S9):167–175Google Scholar
  27. 27.
    Grace Kanmani Prince P, Hemamalini R, Immanuel Rajkumar R (2014) LabVIEW based abnormal muscular movement and fall detection using MEMS accelerometer during the occurrence of seizure. Ind J Sci Technol 7(10):1625–1631Google Scholar
  28. 28.
    Madgwick S (2010) An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Computer Science and Engineering, University of Washington, pp 1–32Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vaishna S. Kumar
    • 1
  • Kavan Gangadhar Acharya
    • 2
  • B. Sandeep
    • 3
  • T. Jayavignesh
    • 1
  • Ashvini Chaturvedi
    • 2
  1. 1.School of Electronics EngineeringVIT UniversityChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringNITKSuratkalIndia
  3. 3.ETP3, Robert Bosch Engineering and Business Solutions Private LimitedBangaloreIndia

Personalised recommendations