A one-class-classification approach to create a stresslevel curve plotter through wearable measurements and behavioral patterns

• Published in: International journal on interactive design and manufacturing 2021-09, Vol.15 (2-3), p.159-171
• Main Authors: Ramírez-Valenzuela, Rodolfo A., Monroy, Raúl, Loyola-González, Octavio, Godínez, Fernando, Soberanes-Martín, Anabelem
• Format: Article
• Language: English
• Subjects: Academic achievement; CAE) and Design; Camcorders; Computer-Aided Engineering (CAD; Data mining; Datasets; Electronics and Microelectronics; Engineering; Engineering Design; Industrial Design; Instrumentation; Learning; Mechanical Engineering; Mental health; Original Paper; Physiology; Plotting; Psychological stress; Reading; Sensors; Stress; Students
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-021-00765-1

Occupational stress has become an interesting field of research in recent years. Stress in students may yield a decline in academic performance or an increase of a mental issue, hence making of paramount importance the timely diagnosis of stress. Although there exist mechanisms for inferring stress level, most of them: assume the test subject is in a controlled environment; use uncomfortable or unaffordable sensors; or they are applicable only when the subject is at a particular posture. Moreover, to the best of the authors’ knowledge, there is no method capable of plotting a person’s stress level curve on the fly. In this paper, we propose a method capable of doing so; our method combines a set of one-class-classifiers capable of capturing the user stress level according to four strata (Low, Medium–Low, Medium–High, and High). Throughout our research, we have developed a dataset, called Student Resilience , which contains observation of several test subjects carrying a mobile phone, and wearing a wristband. For each test subject our dataset also contains the output of a collection of tests, especially designed to evaluate mental health and self-perceived stress. We have used the survey output as ground truth for validation purposes. Our method was capable of correctly plotting stress for 87% of the days submitted by the test subjects. Additionally, in a further attempt to validate our method, we have used data mining to determine whether a stress plot is likely to be explained by the unique activities carried out by each test subject for a given day of the week.