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Hardware/Software Co-Design of a Low-Power IoT Fall Detection Device

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Internet of Things. Technology and Applications (IFIPIoT 2021)

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Abstract

Falls can cause severe injuries to elder people. Many studies have been conducted in order to develop devices that improve the recognition of fall events, while very few have demonstrated approaches for the minimization of their power consumption. We propose a hardware/software co-design of an Internet of Things (IoT) fall detection device that takes advantage of accelerometer’s embedded functionalities and enables its interrupt-driven operation while the rest of the circuit is in shutdown mode. In contrast with most low power fall detection devices, the proposed device supports Wi-Fi connectivity that enables ubiquitous use and real-time remote fall events monitoring through cloud services, without the need of external equipment or a local server. The device integrates a vibration motor and a cancelation button, which notify the detection of a fall event and waits for Patient’s response, while the implementation of battery monitoring mechanism enables continuous device use.

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Notes

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References

  1. Tinetti, M.E., Williams, C.S.: Falls, injuries due to falls, and the risk of admission to a nursing home. N. Engl. J. Med. 337, 1279–1284 (1997). https://doi.org/10.1056/NEJM199710303371806

    Article  Google Scholar 

  2. Spritzer, S.D., et al.: Fall prevention and bathroom safety in the epilepsy monitoring unit. Epilepsy Behav. 48, 75–78 (2015). https://doi.org/10.1016/j.yebeh.2015.05.026

    Article  Google Scholar 

  3. Wang C, et al.: A low-power fall detection algorithm based on triaxial acceleration and barometric pressure. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 570–573 (2014). https://doi.org/10.1109/EMBC.2014.6943655

  4. Wang, C., et al.: A low-power fall detector balancing sensitivity and false alarm rate. IEEE J. Biomed. Heal Inform. 22, 1929–1937 (2018). https://doi.org/10.1109/JBHI.2017.2778271

    Article  Google Scholar 

  5. Wang, C., et al.: Low-power fall detector using triaxial accelerometry and barometric pressure sensing. IEEE Trans. Ind. Inform. 12, 2302–2311 (2016). https://doi.org/10.1109/TII.2016.2587761

    Article  Google Scholar 

  6. de Quadros, T., Lazzaretti, A.E., Schneider, F.K.: A movement decomposition and machine learning-based fall detection system using wrist wearable device. IEEE Sens. J. 18, 5082–5089 (2018). https://doi.org/10.1109/JSEN.2018.2829815

    Article  Google Scholar 

  7. He, J., Zhang, Z., Yu, W.: Interrupt-driven fall detection system realized via a Kalman filter and kNN algorithm. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp 579–584 (2018). https://doi.org/10.1109/SmartWorld.2018.00120

  8. He, J., Zhang, Z., Wang, X., Yang, S.: A low power fall sensing technology based on FD-CNN. IEEE Sens. J. 19, 5110–5118 (2019). https://doi.org/10.1109/JSEN.2019.2903482

    Article  Google Scholar 

  9. Zhuang, W., et al.: A novel wearable smart button system for fall detection. In: AIP Conference Proceedings, p. 020075 (2017). https://doi.org/10.1063/1.4982440

  10. López, A., Pérez, D., Ferrero, F.J., Postolache, O.: A Real-time algorithm to detect falls in the elderly. In: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp 1–5 (2018). https://doi.org/10.1109/MeMeA.2018.8438747

  11. Yuan, J., Tan, K.K., Lee, T.H., Koh, G.C.H.: Power-efficient interrupt-driven algorithms for fall detection and classification of activities of daily living. IEEE Sens. J. 15, 1377–1387 (2015). https://doi.org/10.1109/JSEN.2014.2357035

    Article  Google Scholar 

  12. Ren, L., Peng, Y.: Research of fall detection and fall prevention technologies: a systematic review. IEEE Access 7, 77702–77722 (2019). https://doi.org/10.1109/ACCESS.2019.2922708

    Article  Google Scholar 

  13. Wu, F., Zhao, H., Zhao, Y., Zhong, H.: Development of a wearable-sensor-based fall detection system. Int. J. Telemed. Appl. 2015, 1–11 (2015). https://doi.org/10.1155/2015/576364

    Article  Google Scholar 

  14. Xiuping, Y., Jia-Nan, L., Zuhua, F.: Hardware design of fall detection system based on ADXL345 sensor. In: 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp 446–449 (2015). https://doi.org/10.1109/ICICTA.2015.117

  15. Apple Watch - Battery - Apple. https://www.apple.com/watch/battery/. Accessed 24 Feb 2021

  16. Analog Devices ADXL345 Datasheet. https://www.analog.com/media/en/technical-documentation/data-sheets/ADXL345.pdf. Accessed 10 Jun 2021

  17. Jia, N.: Detecting human falls with a 3-axis digital accelerometer. Analog Dialogue 43, 3–9 (2009)

    Google Scholar 

  18. Espressif Systems: ESP32-WROOM-32 Datasheet (2021). https://www.espressif.com/sites/default/files/documentation/esp32-wroom-32_datasheet_en.pdf. Accessed 10 Jun 2021

  19. What is Adafruit IO? | Welcome to Adafruit IO | Adafruit Learning System. https://learn.adafruit.com/welcome-to-adafruit-io/what-is-adafruit-io. Accessed 24 Feb 2021

  20. Ur, B., et al.: Trigger-action programming in the wild: an analysis of 200,000 IFTTT recipes. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, pp. 3227–3231 (2016). https://doi.org/10.1145/2858036.2858556

  21. Battery Life Calculator | DigiKey Electronics. https://www.digikey.com/en/resources/conversion-calculators/conversion-calculator-battery-life. Accessed 31 Mar 2021

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Acknowledgment

This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-02506).

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Correspondence to Dimitrios Karagiannis .

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Karagiannis, D., Maglogiannis, I., Nikita, K.S., Tsanakas, P. (2022). Hardware/Software Co-Design of a Low-Power IoT Fall Detection Device. In: Camarinha-Matos, L.M., Heijenk, G., Katkoori, S., Strous, L. (eds) Internet of Things. Technology and Applications. IFIPIoT 2021. IFIP Advances in Information and Communication Technology, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-030-96466-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-96466-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96465-8

  • Online ISBN: 978-3-030-96466-5

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