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Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Author

Listed:
  • Sadik Kamel Gharghan

    (Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad 10013, Iraq)

  • Saleem Latteef Mohammed

    (Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad 10013, Iraq)

  • Ali Al-Naji

    (Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad 10013, Iraq
    School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia)

  • Mahmood Jawad Abu-AlShaeer

    (Department of Statistic, Al-Rafidain University College, Baghdad 10064, Iraq)

  • Haider Mahmood Jawad

    (Department of Computer Communication Engineering, Al-Rafidain University College, Baghdad 10064, Iraq)

  • Aqeel Mahmood Jawad

    (Department of Computer Communication Engineering, Al-Rafidain University College, Baghdad 10064, Iraq)

  • Javaan Chahl

    (School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
    Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne, VIC 3207, Australia)

Abstract

Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.

Suggested Citation

  • Sadik Kamel Gharghan & Saleem Latteef Mohammed & Ali Al-Naji & Mahmood Jawad Abu-AlShaeer & Haider Mahmood Jawad & Aqeel Mahmood Jawad & Javaan Chahl, 2018. "Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network," Energies, MDPI, vol. 11(11), pages 1-32, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2866-:d:177672
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    References listed on IDEAS

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    1. Omar Aziz & Jochen Klenk & Lars Schwickert & Lorenzo Chiari & Clemens Becker & Edward J Park & Greg Mori & Stephen N Robinovitch, 2017. "Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    2. Shuyue Wu, 2018. "A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 9(1), pages 1-19, January.
    3. Dongha Lim & Chulho Park & Nam Ho Kim & Sang-Hoon Kim & Yun Seop Yu, 2014. "Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-8, September.
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