A ZigBee-based location-aware fall detection system for improving elderly telecare

Chih Ning Huang, Chia Tai Chan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageEnglish
Pages (from-to)4233-4248
Number of pages16
JournalInternational journal of environmental research and public health
Issue number4
StatePublished - 16 Apr 2014


  • Accelerometer
  • Fall detection
  • Indoor positioning
  • Pervasive healthcare
  • ZigBee


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