References
- Zhou Jinghong. Prevention and control measures of firefighters’ casualties in fire-fighting and rescue [J]. Today’s Firefighting, 2022, 7(08): 133–135.
- Shi Xiaodong,Yang Shikun. A Review of Research on Multi-Sensor Information Fusion [J]. Communication and Information Technology, 2022, 260(06): 34–41.
- Yu Guipeng. Research on the safety of firefighters in fire fighting and rescue work [J]. Today’Real Time Monitoring;s Firefighting, 2023, 8(02): 124–126.
- Shi Xiaodong, Yang Shikun. A review of multi-sensor information fusion research [J]. Communication and Information Technology, 2022, 260(06): 34–41.
- Smith Denise L, Haller Jeannie M, Dolezal Brett A, Cooper Christopher B, Fehling Patricia C. Evaluation of a wearable physiological status monitor during simulated fire fighting activities. [J].Journal of occupational and environmental hygiene, 2014, 11(7): 156–160.
- Bu Y, Wu W, Zeng X, et al. A Wearable Intelligent System For Real Time Monitoring Firefighter’s Physiological State and Predicting Dangers[C]// Hangzhou Dianzi University, Chinese Institute of Electronics. Proceedings of 2015 IEEE 16th International Conference on Communication Technology (ICCT).Institute of Electrical and Electronics Engineers, 2015: 451–454.
- Alessandra A, Matteo C,A. I C, et al. Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques [J]. Sensors, 2021, 21(3).
- YANG Shufeng, SUI Hulin, LI Zhigang. Design and realization of firefighter vital signs monitoring system [J]. Fire Science and Technology, 2014, 33(03): 314317.
- LI Haoze, LI Penghui. Analysis of firefighter casualty causes and prevention countermeasures in firefighting and rescue [J]. Firefighting World (Electronic Edition), 2022, 8(13):62–63.
- Yu Jiaming. Design of firefighter status monitoring and analysis system based on smart wearable device sensing [J]. Firefighting World (Electronic Edition), 2022, 8(06): 72–74.
- Zhu Juxiang, Gu Wei, Luo Danyue et al. Multi-sensor data fusion based on PSO optimized BP neural network [J]. China Test, 2022, 48(08):94–100.
- Ntanasis, P., Pippa, E., Özdemir, A. T., Barshan, B., & Megalooikonomou, V. (2017).Investigation of Sensor Placement for Accurate Fall Detection. Wireless Mobile Communication and Healthcare, 225–232.
- Zhou, T. Y.. Research on multi-sensor data fusion based on fuzzy theory and neural network [D]. Nanjing University of Information Engineering, 2022, 10(25): 124–129.
- Li Si-Nan, Zhao H. Human physiological state based on multi-sensor [D]. Multi-sensor based visualization technique for human physiological state discrimination [J]. Sensors and Microsystems, 2019, 38 (12):25–28+32.
- Ling Xiao, Xu Lushuai, Yu Jianping, Liang Rui. Corrosion rate prediction in oil pipelines based on improved BP neural network [J].Sensors and Microsystems, 2021, 40(02): 124–127.
- Li Y, Zhuohui L, Weijian R, et al. Improving the Drilling Parameter Optimization Method Based on the Fireworks Algorithm [J]. ACS omega, 2022, 7(42):21–26.
- ZHOU Lin, LEI Liping, YANG Longfeng. Multisensor based human behavior recognition system [J]. Sensors and Microsystems, 2016, 35(03):89–91+95.
- Zhang Lei, Chen Feng, Yang Wenhao, Li Hongzhen. Design of fall remote alarm system based on six-axis attitude sensor [J]. Technology and Innovation, 2022(02): 57–60.