School of Computer Science, Faculty of Science and Engineering, The University of Nottingham Ningbo China, Ningbo 315100, China.
Ningbo Municipal Public Security Fire Brigade, Haishu Detachment, Ningbo 315100, China.
Sensors (Basel). 2021 Oct 12;21(20):6770. doi: 10.3390/s21206770.
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.
在过去的十年中，跌倒已成为中国消防员死亡的三大原因之一。尽管有许多关于跌倒检测系统（FDS）的研究，但大多数都使用单个运动传感器。此外，很少有现有的研究考虑了传感器位置和定位对跌倒检测性能的影响；大多数研究都针对老年人的跌倒检测。不幸的是，火灾现场的地板裂缝和不稳定的建筑物结构增加了检测消防员跌倒的难度。特别是，消防员的运动活动更加多样化；因此，将类似跌倒的活动与实际跌倒区分开来是一个重大挑战。本研究通过将运动传感器集成到消防员的个人防护服装的胸部、肘部、手腕、大腿和脚踝上，提出了一种用于消防员跌倒检测的智能可穿戴 FDS。所提出的多传感器递归神经网络检测消防员的跌倒活动，并研究了不同身体部位的惯性测量单元（IMU）的不同组合的性能。结果表明，来自五个拟议身体部位的 IMU 的传感器融合在准确性、灵敏度和特异性方面分别达到了 94.10%、92.25%和 94.59%。
