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Task Phase Recognition for Highly Mobile Workers in Large Building Complexes

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  • Allan Stisen
  • ,
  • Andreas Mathisen
  • ,
  • Søren Krogh
  • Henrik Blunck
  • ,
  • Mikkel Baun Kjærgaard
  • ,
  • Thor Siiger Prentow
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 coworkers’
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 F1-Scores of 89.2%.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom 2016)
Number of pages9
Publication year2016
ISBN (Electronic)978-1-4673-8779-8
Publication statusPublished - 2016
EventIEEE International Conference on Pervasive Computing and Communications - Sydney, Australia
Duration: 14 Mar 201618 Mar 2016


ConferenceIEEE International Conference on Pervasive Computing and Communications

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