title: A ZigBee-based location-aware fall detection system for improving elderly telecare.
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.
authors.affiliation: Institute of Biomedical Engineering, National Yang-Ming University, No.155, Section 2, Linong Street, Taipei, 112 Taiwan. g39604001@ym.edu.tw.
authors.forename: Chih-Ning
authors.initials: CN
authors.lastname: Huang
authors.affiliation: Institute of Biomedical Engineering, National Yang-Ming University, No.155, Section 2, Linong Street, Taipei, 112 Taiwan. ctchan@ym.edu.tw.
authors.forename: Chia-Tai
authors.initials: CT
authors.lastname: Chan
mesh_terms: D061725:Accelerometry; D000058:Accidental Falls; D000368:Aged; D000465:Algorithms; D006801:Humans; D013686:Telemetry
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