Citation

Chai X, Wu R, Pike M, Jin H, Chung WY, Lee BG. Sensors (Basel) 2021; 21(20): e6770.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s21206770

PMID

34695983

Abstract

During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter's personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter's fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively.

Language: en

Keywords

deep learning; fall detection system; inertial measurement unit (IMU); multisensory fusion; wearable IOT technology