Currently, the Internet of Things (IoT) has gained attention for its capability for real-time monitoring. The advancement in sensor and wireless communication technology has led to the widespread adoption of IoT technology in distinct applications. The cloud server, in conjunction with the IoT, enables the visualization and analysis of real-time sensor data. The literature concludes that there is a lack of remote stress-monitoring devices available to assist doctors in observing the real-time stress status of patients in the hospital and in rehabilitation centers. To overcome this problem, we have proposed the use of the IoT and cloud-enabled stress devices to detect stress in a real-time environment. The IoT-enabled stress device establishes piconet communication with the master node to allow visualization of the sensory data on the cloud server. The threshold value (volt) for real-time stress detection by the stress device is identified by experimental analysis using MATLAB based on the results obtained from the performance of three different physical-stress generating tasks. In addition, the stress device is interfaced with the cloud server, and the sensor data are recorded on the cloud server. The sensor data logged into the cloud server can be utilized for future analysis.

Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic skin reaction (GSR) sensor is proposed and built for real-time stress monitoring. The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system, physical stress is created on 10 different participants with three distinct tasks namely reading, visualizing the timer clock, and watching videos. MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e., relaxed for <1.75 volts; Normal: between 1.75 and 1.44 volts and stressed: >1.44 volts. In addition, LabVIEW is used as a data acquisition system, and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.

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