An Implementation Study of a Portable Wearable Stress Measurement System

1. BackgroundStress is a pervasive part of the modern fast-paced life. Additionally, there is a growing body of scientific research indicating that the elevated, semi-permanent stress levels many of us are leading to a variety of health problems. The term stress, as it is currently used was coined by Hans Selye in 1936, who defined it as the non-specific response of the body to any demand for change.Stress is a response to particular events. It is the way our body prepares itself to face a difficult situation with focus, strength and heightened alertness. When we perceive a threat, our nervous system responds by releasing a flood of stress hormones, including adrenaline and cortisol. These hormones rouse the body for emergency action. In some cases it is necessary to collect feedback in order to control this symptom becauseit can become dangerous in certain situations. Therefore, it is necessary to build a device to detect stress. For this objective, we have explored the use of a Galvanic Skin Response (GSR) device in order to detect the variation of conductance of the skin when a person is under stress [1].There are studies which indicate that stress increases the risk of cardiac problems and even changes the experience of individual from an event [2]. In this study we have designed and built a stress sensor based on GSR, and communication unit based on LoRa module. 2. Method We present a prototype of a micro-controller based stress detection system that uses heart rate, respiratory rate, and skin conductance to detect stress. Heart rate is been detected by a constant current green LED and a detector that is placed on the patients finger. Detection of respiratory rate is done by a SpO2 module emitting and receiving red and IR lights. The novelty for measuring the stress is using 4 sets of electrodes on different parts of body and with the help of differential measurement, making it more precise. One electrode is placed on finger, two of them are placed on forehead and the last one is placed on auricular area Fig. 1. In order to check the device performance, we have collected data 16 case studies (eight women and eight men) that completed different tests requiring a certain degree of physical or mental activity, such as mathematicaloperations or breathing deeply. By using a wireless communication system, the user is provided with a certain degree of freedom when using the device and we choose LoRa as the main module for communication due to the low power consumption and the efficiency for low data rate considering the heath data application Fig. 2.We place 4 sets of GSR sensors to left hand fingers, post auricular and forehead areas as parts of the lymphatic system and an Infiniti Biograghs GSR sensor on right hand fingers as a gold standard to validate the collect data while users participating in a standard stress test of Biograph system Fig. 3. 3. ResultsIn the article, we propose an implementation of stress measurement device to track level of stress for an individual in order to detect emotional state Fig. 4. The module uses 4 sets of electrodes to collect differentiated skin conductivity data and compensate it to detect stress levels of the individual. The electrodes are placed on different part of body: finger, forehead and auricular area to give more precise measurement.4. Conclusions In conclusion, the collected stress level measurement shows to be useful to detect an individuals emotional state and this can be done with the proposed wearable in a comfortable condition for the patient 5. References 1. Carter Mundell, Juan Pablo Vielma and Tauhid Zaman, Predicting Performance under Stressful Conditions Using Galvanic Skin Response , In Human-Computer Interaction in https://arxiv.org/abs/1606.01836v1, Jun 2016.2. Maria Viqueira Villarejo, Begona Garcia Zapirain, and Amaia Mendez Zorrilla, A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee , In IEEE Sensors (Basel), pp 6075-6101, May 2012. 3. Mingyang Liu, Di Fan, Xiaohan Zhang and Xiaopeng Gong, Human Emotion Recognition Based on Galvanic Skin Response Signal Feature Selection and SVM , In International Conference on Smart City and SystemsEngineering (ICSCSE), Nov 2016.