Activity-logging for self-coaching of knowledge workers

With an increased societal focus on a sustainable economy and a healthy population, well-being of knowledge workers has become an important topic. This paper investigates techniques to support a knowledge worker to manage his well-being. A possible solution is to monitor the workers’ behaviour and use this information for giving feedback as soon as his well-being is threatened. Knowledge workers use a broad range of communication means to achieve their goals, like a computer and mobile phone. Our research aims at using features like mouse clicks, active applications or key presses, because these are rather simple features to obtain instead of more invasive tools like a heart-rate monitor. This paper presents the first results of our research. First, logging of low-level features is developed. Based on these features the behaviour of different users is investigated. At first sight, this behaviour seems to be rather chaotic, but by taking into account different tasks, more structure is observed within the data. This paper shows that different behaviour is observed for different users and different tasks, while the same characteristics are observed when a user is performing the same task. This suggests that also anomalous behaviour might be recognized, which is an important result for developing self-coaching tools.