Abstract
This paper presents the design process and the results of a novel fall
detector designed and constructed at the Faculty of Electronics, Military
University of Technology. High sensitivity and low false alarm rates were
achieved by using four independent sensors of varying physical quantities
and sophisticated methods of signal processing and data mining. The
manuscript discusses the study background, hardware development,
alternative algorithms used for the sensor data processing and fusion for
identification of the most efficient solution and the final results from
testing the Android application on smartphone. The test was performed in
four 6-h sessions (two sessions with female participants at the age of 28
years, one session with male participants aged 28 years and one involving
a man at the age of 49 years) and showed correct detection of all 40
simulated falls with only three false alarms. Our results confirmed the
sensitivity of the proposed algorithm to be 100% with a nominal false
alarm rate (one false alarm per 8 h).
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