Investigating real-time monitoring of fatigue indicators of New Zealand forestry workers
Bowen, J., Hinze, A., & Griffiths, C. J. G. (2019). Investigating real-time monitoring of fatigue indicators of New Zealand forestry workers. Accident Analysis and Prevention, 126, 122–141. https://doi.org/10.1016/j.aap.2017.12.010
Permanent Research Commons link: https://hdl.handle.net/10289/12964
The New Zealand forestry industry has one of the highest fatality and injury rates of any industrial sector in the country. Worker fatigue has been identified as one of the main contributing factors. Currently no independent and objective large data source is available that might support an analysis of this, or provide the basis for ongoing monitoring to further investigate. In order to successfully manage fatigue in the forestry workplace, we must identify suitable ways of detecting it. Industry partners are increasingly looking at monitoring solutions (particularly lightweight, wearable technology) that aim to measure worker activities and physiological metrics in order to determine if they are fatigued. In this article we present the results of studies which investigate whether or not such technology can capture meaningful data in a reliable way that is both practical and usable within the forestry domain. Two series of studies were undertaken with in-situ forestry workers using reaction and decision-making times as a measure of potential impairment, while considering activity levels (via step count and heart rate) and job-roles. We present the results of these studies and further provide a comparison of results across different ambient temperatures (winter vs. summer periods). The results of our studies suggest that it may not be possible to identify correlations between workloads (based on both physical and cognitive stresses) and fatigue measures using in-situ measurements as results are highly personalised to individual workers and can be misleading if the wider context is not also taken into consideration.
This is an author’s submittedd version of an article published in the journal: Accident Analysis and Prevention. © 2017 Elsevier.