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.