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A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare

Author

Listed:
  • Chih-Ning Huang

    (Institute of Biomedical Engineering, National Yang-Ming University, No.155, Section 2, Linong Street, Taipei, 112 Taiwan)

  • Chia-Tai Chan

    (Institute of Biomedical Engineering, National Yang-Ming University, No.155, Section 2, Linong Street, Taipei, 112 Taiwan)

Abstract

Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k -nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person.

Suggested Citation

  • Chih-Ning Huang & Chia-Tai Chan, 2014. "A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare," IJERPH, MDPI, vol. 11(4), pages 1-16, April.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:4:p:4233-4248:d:35154
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    References listed on IDEAS

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    1. Mark V Albert & Konrad Kording & Megan Herrmann & Arun Jayaraman, 2012. "Fall Classification by Machine Learning Using Mobile Phones," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    2. Wolfgang Lutz & Warren Sanderson & Sergei Scherbov, 2008. "The coming acceleration of global population ageing," Nature, Nature, vol. 451(7179), pages 716-719, February.
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    1. Dora Cama-Pinto & Miguel Damas & Juan Antonio Holgado-Terriza & Francisco Gómez-Mula & Alejandro Cama-Pinto, 2019. "Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse," IJERPH, MDPI, vol. 16(10), pages 1-15, May.

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