Real time fall detection in fog computing scenario


Ambient assisted living is a concept which uses information and communication technology to assist the daily living of people. Human fall detection is an important sub-area of ambient assisted living. Human fall has been seen as a critical problem for elderly people. Fall detection is an approach which analyzes sensor data (wearable sensors/ambient sensors or vision-based sensor) to detect human fall using various learning algorithms. This paper presents a fall detection method that detects and notifies fall activity in real-time using fog computing. Support Vector Machine based one class classification is used here to build fall detection model. Five features have been calculated from Smartphone accelerometer data to build fall detection model. To implement one class classification, a new method for kernel matrix calculation is proposed here. This fall detection model exploited the concept of fog computing to send real-time notification to the caregiver and it is also able to notify caregiver in absence of fog node to cloud connection. In the proposed method we have got 100% sensitivity and 98.77% specificity for human fall detection. This fall detection method is also tested on real fall data and it is found that this method is able to detect 100% fall activities. Use of fog computing concept drastically reduces amount of data transferred to the cloud from 900 values (10,799 bytes) to 5 values (59 bytes) per 6 s.

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  1. 1.

    Rubenstein, L.Z.: Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing 35, ii37–ii41 (2006)

    Article  Google Scholar 

  2. 2.

    Ageing and health, [Online]. Accessed 03 Dec 2018 (2018)

  3. 3.

    Define IoT - IEEE Internet of Things, [Online]. Accessed 04 Jan 2019

  4. 4.

    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)

  5. 5.

    Fog Computing Market Size | Global Industry Forecast 2026, [Online]. Accessed 04 Jan 2019

  6. 6.

    Gope, P., Hwang, T.: BSN-Care: a secure IoT-based modern healthcare system using body sensor network. IEEE Sens. J. 16, 1368–1376 (2015)

    Article  Google Scholar 

  7. 7.

    Tyagi, S., Agarwal, A., Maheshwari, P.: A conceptual framework for IoT-based healthcare system using cloud computing, 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 503–507, IEEE, India (2016)

  8. 8.

    Internet of Things (IoT) Healthcare Market Size, Industry Analysis, Forecast 2021, [Online]. Accessed 04 Jan 2019

  9. 9.

    Greene, S., Thapliyal, H., Carpenter, D.: IoT-based fall detection for smart home environments. In 2016 IEEE international symposium on nanoelectronic and information systems (iNIS), pp. 23–28, IEEE, India (2016)

  10. 10.

    Gia, T.N., Tcarenko, I., Sarker Victor, K., Rahmani, A.M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Iot-based fall detection system with energy efficient sensor nodes. In 2016 IEEE Nordic Circuits and Systems Conference (NORCAS), pp. 1–6, IEEE, India (2016)

  11. 11.

    Tcarenko, I., Gia, T.N., Tcarenko, I., Sarker, V.K., Rahmani, A.M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Energy-efficient iot-enabled fall detection system with messenger-based notification. In International Conference on Wireless Mobile Communication and Healthcare, pp. 19–26, Springer, India (2016)

  12. 12.

    Hsieh, Y.-Z., Jeng, Y.-L.: Development of home intelligent fall detection IoT system based on feedback optical flow convolutional neural network. IEEE Access 6, 6048–6057 (2017)

    Article  Google Scholar 

  13. 13.

    Ngu, A., Yeahuay, W., Zare, H., Polican, A., Yarbrough, B., Yao, L.: Fall detection using smartwatch sensor data with accessor architecture. International Conference on Smart Health, pp. 81–93. Springer, India (2017)

  14. 14.

    Hsu, C.C.-H., Wang, M.Y.-C., Shen, H.C.H., Chiang, R.H-C., Wen, C.H.P.: FallCare+: an IoT surveillance system for fall detection. In 2017 International Conference on Applied System Innovation (ICASI), pp. 921–922, IEEE, India (2017)

  15. 15.

    Mauldin, T., Canby, M., Metsis, V., Ngu, A., Rivera, C.: SmartFall: a smartwatch-based fall detection system using deep learning. Sensors 18, 3363 (2017)

    Article  Google Scholar 

  16. 16.

    Yacchirema, D., Suárez, J., de Puga, C., Palau, M.E.: Fall detection system for elderly people using IoT and big data. Proced. Comput. Sci. 130, 603–613 (2018)

    Article  Google Scholar 

  17. 17.

    Chandra, I., Sivakumar, N., Gokulnath, C.B., Parthasarathy, P.: IoT based fall detection and ambient assisted system for the elderly. Clust. Comput. 22, 2517–2525 (2019)

    Article  Google Scholar 

  18. 18.

    Medrano, C., Igual, R., Plaza, I., Castro, M.: Detecting falls as novelties in acceleration patterns acquired with smartphones. PLoS ONE 9, e94811 (2014)

    Article  Google Scholar 

  19. 19.

    Klenk, J., Schwickert, L., Palmerini, L., Mellone, S., Bourke, A., Ihlen, E.A.F., Kerse, N., Hauer, K., Pijnappels, M., Synofzik, M., et al.: The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls. Eur. Rev. Aging Phys. Act. 13, 8 (2016)

    Article  Google Scholar 

  20. 20.

    Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12, 1207–1245 (2000)

    Article  Google Scholar 

  21. 21.

    Li, P., Samorodnitsk, G., Hopcroft, J.: Sign cauchy projections and chi-square kernel. In Advances in Neural Information Processing Systems, pp. 2571–2579 (2013)

Download references


We thank all participating men and women in the FARSEEING project, as well as all FARSEEING research scientists, study and data managers and clinical and administrative staff who make the study possible.



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Correspondence to Rashmi Shrivastava.

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Shrivastava, R., Pandey, M. Real time fall detection in fog computing scenario. Cluster Comput 23, 2861–2870 (2020).

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  • Fall detection
  • Fog computing
  • One-class classification
  • SVM