Research on the method of abnormal physiological indicators diagnosis for subway drivers.

Jian Deng; Ziyan Ma; Rongshu Zheng

doi:10.1117/12.3030515

16 May 2024 Research on the method of abnormal physiological indicators diagnosis for subway drivers.

Jian Deng, Ziyan Ma, Rongshu Zheng

Proceedings Volume 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024); 1316010 (2024) https://doi.org/10.1117/12.3030515
Event: 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 2024, Beijin, China

Abstract

A real-time physiological index anomaly detection method for subway drivers has been proposed. We use smart wristbands to collect indicator data and determine whether the current physiological indicator values of subway drivers are in an abnormal state. We use historical data of three human physiological indicators, heart rate, body temperature, and respiration, and generate confidence intervals for various indicators of drivers based on the principle of statistical normal distribution, to establish a model for judging abnormal physiological indicators of drivers. Python programming is applied to simulate historical data, establish confidence intervals, collect indicator data, and calculate results for judgment. The simulation results can determine the current state of the driver through measurement values, and the historical data generated by the measurement is in a continuously updated state. The confidence intervals of each indicator can be continuously adjusted, which is more applicable than other methods. Moreover, monitoring indicators can be added on the basis of technology and equipment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian Deng, Ziyan Ma, and Rongshu Zheng "Research on the method of abnormal physiological indicators diagnosis for subway drivers.", Proc. SPIE 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 1316010 (16 May 2024); https://doi.org/10.1117/12.3030515

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