Stress Tracking in Learning Activities — Stila

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Stress is ubiquitous
in our contemporary society

Stress is a common problem in our contemporary society, especially among students and professionals. The pressure and demand to deliver excellent results in their studies or at their work places is constantly growing. To overcome this pressure, plenty of them are using wearables as smart watches or fitness tracker along with their smartphones to monitor their daily activities and to measure physiological data. These data collected can provide valuable outcome to detect and prevent from possible stressors, but there is a lack of mobile applications unifying the variety of measurements to an user-friendly and self-supporting tool.

The goal of project Stila is to provide students and professionals with personalized recommendations aiming at improving their learning performances. To achieve this goal, stress development of students and professionals needs to be monitored during theirs learning activities. Project Stila contributes a mobile application, which uses heart rate data provided by state-of-the-art fitness tracker or smart watches to measure computed stress, that is defined as the stress computationally derived from instantaneous measures of stress symptoms obtained by non-invasive methods.

Stila strives to give students and professionals visual perception of computed stress

Improving Awareness

Stila android mobile application presents the computed stress by examining users' heart rate variability and display the computed stress in a user friendly graphical representation.

User can interact with displayed elements within this graphical representation. This sort of interactions transform intangible computed stress measurements to touchable subjects. Users can thus perceive their computed stress in a playful way.

Enhanced User Experience

Initially, the accuracy and usability of several fitness tracker has been examined in a pilot study and afterwards evaluated to derive heart rate variability scores.

Our team believe to model computed stress adapted to individual heart rate variability scores. A history view of computed stress development is provided to allow users gain more information about their own physiological reactions to the changing environment.

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Linking Undertaken Activities

The development of stress may vary among the facets of undertaken activities. For this reason, a linking between the stress development and undertaken activities is vital for providing appropriate recommendations to users.

Stila android mobile application allows users to create these links in a fast and an userfriendly approach . In this way, users can conduct detailed analysis of their stress development by undertaken activities.

Detecting Stress in Learning

A detailed Profile is integrated with stress level detection for learning activities. With further analysis, more detailed feedback to learning behaviours can be provided to users.

Stila strives to provide feedbacks about eustress and distress


The number of students at universities increases every year. It is normal that a lecturer teaches to more than one hundred students in a single lecture. This setup is disadvantageous for the teaching quality of mass universities since the lecturers are not able to care for every student. Therefore, it would be beneficial for lecturers to receive a fast and direct feedback about the emotional state of the students during lectures (or while learning). This feedback could help lecturers to adapt the learning content. Also for the students themselves, a personalized feedback about their emotional state could help to adapt their learning behavior.


An especially important and relevant aspect in this context is the stress level of the students, which influences the effectiveness of their learning. Stress can be differentiated between negative stress (distress), which negatively influences the learning and positive stress (eustress), which is even beneficial for the learning success.

In a further stage of stila project, research about the facets of stress need to be conducted to distinguish distress from eustress, based on physiological and physical data.

History of Activities

A history view of activities is available through the online portal of project Stila. One's observation of eustress and distress usually doesn't last longer than a day. The history view of activities helps one to retrace the daily experience over a long period of time.


Self-Assessment vs. System Prognosis

The self-assessment of positive-, negative-, neutral moods of the user is displayed with the labels of prognosis generated by Stila portal using various Machine Learning Algorithms. User can easily compare these two kind of labels and confirm or even reject prognoses made by the system. Further confirmation and rejection creates new labels for Stila portal to learn with Supervised Machine Learning Algorithms.