2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
March 14, 2016 to March 19, 2016
Allan Stisen , Department of Computer Science, Aarhus University, Denmark
Andreas Mathisen , Department of Computer Science, Aarhus University, Denmark
Soren Krogh Sorensen , Department of Computer Science, Aarhus University, Denmark
Henrik Blunck , Department of Computer Science, Münster University, Germany
Mikkel Baun Kjargaard , Department of Computer Science, Aarhus University, Denmark
Thor Siiger Prentow , Department of Computer Science, Aarhus University, Denmark
Being aware of activities of co-workers is a basic and vital mechanism for efficient work in highly distributed work settings. Thus, automatic recognition of the task phases the mobile workers are currently (or have been) in has many applications, e.g., efficient coordination of tasks by visualizing co-workers' task progress, automatic notifications based on context awareness, and record filing of task statuses and completions. This paper presents methods to sense and detect highly mobile workers' tasks phases in large building complexes. Large building complexes restrict the technologies available for sensing and recognizing the activities and task phases the workers currently perform as such technologies have to be easily deployable and maintainable at a large scale. The methods presented in this paper consist of features that utilize data from sensing systems which are common in large-scale indoor work environments, namely from a WiFi infrastructure providing coarse grained indoor positioning, from inertial sensors in the workers' mobile phones, and from a task management system yielding information about the scheduled tasks' start and end locations. The methods presented have low requirements on the accuracy of the indoor positioning, 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 labelled real-world data collected over 4 days of regular work life of the mobile workforce. The collected data yields 83 tasks in total involving 8 different orderlies from a major university hospital with a building area of 160, 000 m2. The results show that the proposed methods can distinguish accurately between the four most common task phases present in the orderlies' work routines, achieving Fi-Scores of 89.2%.
Hospitals, Mobile communication, Buildings, IEEE 802.11 Standard, Dispatching, Sensor phenomena and characterization
A. Stisen, A. Mathisen, S. K. Sorensen, H. Blunck, M. B. Kjargaard and T. S. Prentow, "Task phase recognition for highly mobile workers in large building complexes," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-9.