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An IoT based Real-Time Stress Detection System for Fire-Fighters

机译:基于物联网的消防员实时压力检测系统

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Stress is an acute condition of a person experienced under high pressure. This can to conditions like depression and cardiac arrest. One of the top most stressful jobs across the globe is that of firefighters. They, not only have to rescue the people who are in danger also needs to keep themselves and their co-workers safe during the rescue mission. This paper deals with the design and implementation of a real-time stress monitoring system for firemen who have been assigned a fire rescue mission. This work focusses on the development of a wireless sensor node prototype embedded on the gloves of firemen which embeds Galvanic Skin Response (GSR) sensor and heart rate sensor to detect the stress level, a microcontroller to process data, a communication module using ZigBee to send data and a rechargeable long-lasting power supply. The system uses Message Queuing Telemetry Transport (MQTT), as the IoT message protocol and Adafruit IO as the MQTT broker and analytics platform to store and analyze data based on which the alerts are displayed to the fire engine via User Interface (UI). Also, advanced Machine Learning techniques can be incorporated in the future to study the anomaly and predict the stress among firemen beforehand.
机译:压力是在高压下经历的人的急性条件。这可能与抑郁和心脏骤停等条件。全球最紧张的工作之一是消防员。他们不仅必须拯救有危险的人也需要让自己和他们的同事在救援使命期间安全。本文涉及为消防员分配消防救援任务的实时应力监测系统的设计和实施。这项工作侧重于嵌入在消防员手套上的无线传感器节点原型的开发,嵌入电流皮肤响应(GSR)传感器和心率传感器来检测压力水平,微控制器处理数据,使用Zigbee发送的通信模块发送数据和可充电的长持久电源。该系统使用消息排队遥测传输(MQTT),作为IOT消息协议和ADAFRUIT IO作为MQTT代理和分析平台,以存储和分析数据通过用户界面(UI)将警报显示给消防引擎。此外,还可以在未来结合先进的机器学习技术,以研究异常并预先预测消防员之间的压力。

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