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.

Falling is one of the primary causes of firefighter casualties. Therefore, early fall detection, followed by reliable and timely notification, is vital to ensure the survival of firefighters. However, due to complex and diverse firefighting scenarios, current fall detection systems are inadequate, especially in terms of accuracy and real-time applications. Hence, in this study, a wearable pre-impact fall detection system for firefighters is proposed. We also evaluated the feasibility of using machine learning and ensemble learning methods deployed on the edge node to simultaneously improve the real-time performance and accuracy. Moving thresholding method is introduced to overcome the class-imbalanced issue due to fewer samples acquired during the pre-impact phase. Based on 14 firefighters’ data collection, the experimental results revealed that a Decision Tree classifier with optimized parameters (DT-ED4) outperformed other machine learning and ensemble learning methods in a trade-off between processing time and accuracy. The results also showed that our system can detect falls before the impact with an average lead time of 447.9 ms, sensitivity of 95.10%, and specificity of 97.99%. The system’s heterogeneous IoT network architecture facilitated remote monitoring and alerts that enabled the incident commander to execute the rescue mission immediately upon notification. This study enhances the practicability of PIFDS and extends the application range of PIFDS from health-care to firefighting.

With the development of indoor location-based services (ILBS), the dual foot-mounted inertial navigation system (DF-INS) has been extensively used in many fields involving monitoring and direction-finding. It is a widespread ILBS implementation with considerable application potential in various areas such as firefighting and home care. However, the existing DF-INS is limited by a high inaccuracy rate due to the highly dynamic and non-stable stride length thresholds. The system also provides less clear and significant information visualization of a person’s position and the surrounding map. This study proposes a novel wearable-foot IOAM-inertial odometry and mapping to address the aforementioned issues. First, the person’s gait analysis is computed using the zero-velocity update (ZUPT) method with data fusion from ultrasound sensors placed on the inner side of the shoes. This study introduces a dynamic minimum centroid distance (MCD) algorithm to improve the existing extended Kalman filter (EKF) by limiting the stride length to a minimum range, significantly reducing the bias in data fusion. Then, a dual trajectory fusion (DTF) method is proposed to combine the left- and right-foot trajectories into a single center body of mass (CBoM) trajectory using ZUPT clustering and fusion weight computation. Next, ultrasound-type mapping is introduced to reconstruct the surrounding occupancy grid map (S-OGM) using the sphere projection method. The CBoM trajectory and S-OGM results were simultaneously visualized to provide comprehensive localization and mapping information. The results indicate a significant improvement with a lower root mean square error (RMSE = 1.2 m) than the existing methods.