Next Article in Journal
A Survey of the Perception of Comprehensiveness among Dentists in a Large Brazilian City
Next Article in Special Issue
Design of a Real-Time and Continua-Based Framework for Care Guideline Recommendations
Previous Article in Journal
Modifying Health Behavior to Prevent Cardiovascular Diseases: A Nationwide Survey among German Primary Care Physicians
Previous Article in Special Issue
Gait Recognition and Walking Exercise Intensity Estimation
Open AccessArticle

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

Institute of Biomedical Engineering, National Yang-Ming University, No.155, Section 2, Linong Street, Taipei, 112 Taiwan
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(4), 4233-4248;
Received: 19 February 2014 / Revised: 1 April 2014 / Accepted: 3 April 2014 / Published: 16 April 2014
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. View Full-Text
Keywords: fall detection; accelerometer; indoor positioning; ZigBee; pervasive healthcare fall detection; accelerometer; indoor positioning; ZigBee; pervasive healthcare
Show Figures

Figure 1

MDPI and ACS Style

Huang, C.-N.; Chan, C.-T. A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare. Int. J. Environ. Res. Public Health 2014, 11, 4233-4248.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Back to TopTop