An Experimental Platform for Fall Detection Using Beacon, Node MCU and MATLAB

Authors

  • Jovan Djurkovic MECOnet
  • Radovan Stojanović Faculty for Electrical Engineering, University of Montenegro
  • Betim Cico Epoka University

Keywords:

wearables, fall detection, accelerometer, Beacon, MATLAB, ESP Node

Abstract

Healthcare wearables have become very powerful and useful devices capable to detect and monitor numerous health parameters and physical conditions. Fall Detection Systems (FDS) are a part of them, with function to detect the falls, mainly in adults. Real-time falls detection may reduce the risk of major problems and enable protective and medical personnel to act immediately. FDS varies in terms of embedded hardware and software (algorithms). Here, we present a system that can be used as an experimental platform to explore the fall detection algorithms based on inertial methods. Battery powered Bluetooth Low Energy (BLE) Beacon with built in accelerometer is used as a sensor device and data transmitter. BLE ESP based gateway receive the data and forward them to MATLAB host, on which experiments are conducted to find the most suitable algorithms. Later, the algorithms are embedded into suitable platforms, which are incorporated into sensor housings, receiver-indicators or home/hospital care systems. The architecture of overall experimentations system as well as preliminary testing results for accelerometer-based algorithms are presented, but without changing the hardware configuration, other algorithms can also be tested.

References

Stojanović, R., Djurković, J., Mijušković, S., Lutovac, B., & Škraba, A. (2023, June). SYNTROFOS: A Wearable Device for Vital Sign Monitoring, Hardware and Signal Processing Aspects. In 2023 12th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-6). IEEE.

Wang, X., Ellul, J., & Azzopardi, G. (2020). Elderly fall detection systems: A literature survey. Frontiers in Robotics and AI, 7, 71.

Kubov, V. I., Dymytrov, Y. Y., Stojanović, R., Kubova, R. M., & Škraba, A. (2020, June). A Feasible IoT System for Monitoring PPG and ECG Signals by using Low-cost Systems-on-chips and HTML Interface. In 2020 9th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4). IEEE.

https://invensense.tdk.com/products/motion-tracking/6-axis/mpu-6050/

Bagala, Fabio, et al. "Evaluation of accelerometer-based fall detection algorithms on real-world falls." PloS one 7.5 (2012): e37062.

R. Stojanović, A. Škraba and B. Lutovac, "A Headset Like Wearable Device to Track COVID-19 Symptoms," 2020 9th Mediterranean Conference on Embedded Computing (MECO), 2020, pp. 1-4, doi: 10.1109/MECO49872.2020.9134211.

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Published

2023-09-11

How to Cite

Djurkovic, J., Stojanović, R., & Cico, B. (2023). An Experimental Platform for Fall Detection Using Beacon, Node MCU and MATLAB. WiPiEC Journal - Works in Progress in Embedded Computing Journal, 9(2). Retrieved from https://wipiec.digitalheritage.me/index.php/wipiecjournal/article/view/46

Issue

Vol. 9 No. 2 (2023): WiPiEC Journal

Section

Articles

License

Copyright (c) 2023 Jovan Djurkovic; Radovan Stojanović, Betim Cico

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