Different Methods to Detect Stress in Knowledge Workers: A Pilot Study for Long-term Monitoring.

Stress is a common part of working life, but knowledge is lacking on how to identify it early and with little effort on the part of the employee. We investigated whether simple stress reports and computer usage data could be useful tools for long-term assessment of stress in real life. 38 experts responded to a baseline questionnaire on need for recovery (NFR) and psychological distress (General Health Questionnaire, GHQ12). Their computer usage for work was recorded for 5 months, during which they filled in a 4-month simple diary and a 2-week detailed diary on, for example, stress and productivity. Salivary cortisol and heart rate variability were collected on 3 consecutive days. Generalized estimating equations models were used for the analyses. High NFR and GHQ12 predicted self-reported stress during work, and a decrease in (some) mouse usage features, but not keyboard usage features, over the following months. Some mouse usage features were associated with stress and productivity. The results provide some support for the usefulness of simple stress questions and mouse usage features in assessing long-term stress in real life.

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain.

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.