Location

MIT Media Lab, E14-633

Description

Chronic psychological stress carries a wide array of pathophysiological risks, including cardiovascular and cerebrovascular diseases, diabetes, and immune dysregulation. An important step in managing stress, before it becomes chronic, is recognizing precisely when and where it occurs. This thesis creates and evaluates new methods to improve the measurement of stress at work by leveraging state-of-the-art wearable devices.

The first part of the thesis systematically compares gathering self-reported stress levels with head and wrist-worn devices, and compares them to the traditional phone. In particular, 15 participants were asked to carry these devices during 5 days of their regular work day and to self-report their emotional state several times a day with our custom experience sampling application. Hernandez Rivera found that both head and wrist-worn devices significantly outperformed the phone in terms of the amount of answered prompts and the speed to start answering. However, different factors such as interaction types, screen size, and familiarity with the devices affected users’ experience and responses.

The second part of the thesis develops novel methods to comfortably capture physiological signals associated with the stress response. In particular, 36 participants were asked to carry either a head-worn device, a smartwatch or a phone while performing different still body postures in a controlled laboratory study. Using the proposed methods, we demonstrated that wearable motion-sensitive sensors inside these devices can capture heart and breathing rates as accurately as FDA-cleared devices from traditional body locations. Furthermore, using the data collected from the 15 participants, we demonstrated that our methods can be opportunistically used in real-life when people are relatively still. In Hernandez Rivera's study, for instance, the head-worn device provided accurate heart rate assessments around 20% of the work day.

Finally, the third part of the thesis uses supervised learning methods to automatically infer self-reported stress levels from different types of wearable data, including physiological, contextual and behavioral signals. While there is not a one-method-fits-all solution, we automatically identified the subset of signals that best captured stress for each of the 15 participants. Furthermore, Hernandez Rivera characterized many of the challenges that plague the task of real-life automated stress recognition.

Host/Chair: Rosalind W. Picard

Participant(s)/Committee

Pattie Maes, Karen S. Quigley