1 Machine Learning Based Solutions for Real-Time Stress Monitoring Rajdeep Kumar Nath Himanshu Thapliyal University of Kentucky University of Kentucky Allison Caban-Holt Saraju P. Mohanty Wake Forest School of Medicine University of North Texas Abstract— Stress may be defined as the reaction of the body to regulate itself to changes within the environment through mental, physical, or emotional responses. Recurrent episodes of acute stress can disturb the physical and mental stability of a person. This subsequently can have a negative effect on work performance and in the long term can increase the risk of physiological disorders like hypertension and psychological illness such as anxiety disorder. Psycho- logical stress is a growing concern for the worldwide population across all age groups. A reliable, cost- efficient, acute stress detection system could enable its users to better monitor and manage their stress to mitigate its long-term negative effects. In this article, we will review and discuss the literature that has used machine learning based approaches for stress detection. We will also review the existing solutions in the literature that have leveraged the concept of edge computing in providing a potential solution in real-time monitoring of stress. I. WHAT IS STRESS? Stress is defined as the reaction to adverse en- vironmental situations that challenges the typical adaptive capability as perceived by an individual [1]. Although positive stress (eustress) helps the individual to stay focused to deal with adversi- ties, negative stress (distress) causes the activation of the HPA (hypothalamic-pituitary-adrenocortical) axis. Prolonged activation of the HPA axis may cause physiological and psychological disorders [2]. Psychological stress is also found to affect physiological processes and has a negative effect on daily work performance and is thought to affect the national economy [3]. Monitoring negative stress levels can provide useful information for identi- fying the stressors and provide an opportunity to adopt necessary precautions in preventing resulting disruption. Psychological Measures ➢ STAI ➢ POMS ➢ DASS 21 Stress Physiological Measures Blood Pressure Heart Rate Cortisol GSR ECG Objective Subjective Fig. 1: Examples of objective and subjective mea- sures of stress. The two distinct effects of negative stress are defined as: (i) physiological or "objective" stress; and (ii) psychological stress or the "subjective" stress also known as the perceived stress. Objective stress is reflected by the change in physiological measures such as elevated blood pressure, increased heart rate, and increased cortisol levels. Subjec- tive stress is the perception of whether or not a situation as stressful by an individual. The most common method of measuring perceived stress is by employing stress questionnaires like DASS 21 (Depression, Anxiety and Stress Scale - 21 Items), STAI (State-Trait Anxiety Inventory), and POMS (Profile of Mood States) (see Figure 1). Two main physiological measures for stress include: (i) Corti- sol (stress hormones) and (ii) Physiological signal measurements like GSR (Galvanic Skin Response),