Stop motion: using high resolution spatiotemporal data to estimate and locate stationary and movement behaviour in an office workplace

Abstract

Prolonged periods of stationary behaviour, a common occurrence in many office workplaces, are linked with a range of physical disorders. Investigating the physical context of this behaviour may be a key to developing effective interventions. This study aimed to estimate and locate the stationary and movement behaviours of office workers (n = 10) by segmenting spatiotemporal data collected over 5 days in an office work-based setting. The segmentation method achieved a balanced accuracy ≥85.5% for observation classification and ≥90% for bout classification when compared to reference data. The results show the workers spent the majority of their time stationary (Mean = 86.4%) and had on average, 28.4 stationary and 25.9 moving bouts per hour. While these findings accord with other studies, the segmented data was also visualised, revealing that the workers were stationary for periods ≥5 min at multiple locations and these locations changed across time.

Practitioner Summary: This study applied a data segmentation method to classify stationary and moving behaviours from spatiotemporal data collected in an office workplace. The segmented data revealed not only what behaviours occurred but also their location, duration, and time. Segmenting spatiotemporal data may add valuable physical context to aid workplace research.

Keywords:

• Office
• physical inactivity
• sensors
• localization
• accelerometer

Acknowledgements

The authors acknowledge the technical assistance provided by the Sydney Informatics Hub, a Core Research Facility of the University of Sydney. The authors also acknowledge the invaluable assistance of Tom Treffry, Charles Dalrymple-Hay, David Emerson, and Anthony Fawcett.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

The IPS research equipment used in this study was funded through a University of Sydney Commercial Development and Industry Partnerships Grant, which included financial contributions from the University of Sydney, AMP Limited, and LeaseAccelerator (formerly Guardian Global Systems). The funders of the IPS research equipment did not have any role in the design of the study, collection of data, data analysis, preparation of the manuscript nor decision to publish.