Task phase recognition and task progress estimation for highly mobile workers in large building complexes

Abstract

Being aware of the activities of co-workers is a vital mechanism for efficient work in highly distributed work settings. Thus, automatically recognizing which task phases mobile workers are in and estimating their task progress is crucial for many aiding applications (e.g., task scheduling) utilizing coordination mechanisms (e.g., visualization of co-workers’ task progresses and notifications based on context awareness).This paper presents methods to sense and detect highly mobile workers’ task phases and progresses in large building complexes. These methods make use of data from sensing systems common in large-scale indoor work environments, such as WiFi infrastructures providing coarse grained indoor positioning, inertial sensors in the workers’ mobile phones, and from task management systems logging scheduled tasks. The methods presented have low requirements on sensing accuracy and thus come with low deployment and maintenance effort in real-world settings.We evaluated the proposed methods in a large hospital complex, where the highly mobile workers were recruited among the non-clinical workforce. The evaluation is based on manually labeled real-world data collected over 4 days of regular work life. The collected data yields 83 tasks in total involving 8 different employees of a major university hospital with a building area of 160,000 m2. The results show that the proposed methods can with reasonable accuracy i) distinguish between the four most common task phases present in the workers’ routines, achieving F1-Scores of 89.2%, and ii) estimate the task progress, yielding a mean error of 126.82 seconds for estimating the time until task completion and of 9.49 pp for estimating task progress.