Context

In rail, signallers are responsible for achieving a high standard of safety and operational performance. New technologies are being introduced to rail that impact the signalling task, but this impact is not easily measured. Current mental workload measures interrupt the task, and rely on self-assessment. Wearable physiological measures may offer a new assessment method. The specific aim of the PhD is to determine the feasibility of using new wearable physiological measures in rail, including how staff respond to these measures and their suitability for future use in rail.

Background

There is a drive within the rail industry to gain more value from data [1]. This PhD considers what value can be gained from human performance data. Human performance refers here to the act of carrying out a task and the effort required. Distraction and loss of concentration are performance shaping factors known to contribute to rail accidents and incidents [2]. There are no current methods regularly applied in rail to assess changing effort or attention over time. Current workload measures in rail detect overall levels of demand or workload e.g. the Integrated Workload Scale [3]. Physiological measures may support assessing the task, and changing effort over time, without interrupting the task.

Physiological measures detect an aspect of the body e.g. heart rate variability (HRA), or electrodermal activity (EDA) [4]. Physiological measures have been used to infer overall mental workload [5], task load [6], and time pressure [7]. In rail, HRA of drivers reduced at tunnels, and before and after stops [8] and EDA distinguished task demands in a driving simulator [9]. These measures are suitable for tasks, including signalling, which involve the operator sitting down. On a practical note however, the reliability and validity of physiological measures are still being developed.

Method

This research applies a mixed methods approach. Firstly semi-structured interviews were conducted with 15 rail and Human Factors experts. The next research step is to assess the suitability of wearable physiological measures in an initial feasibility study. The research will then assess the feasibility of using these measures in a rail simulator or live environment. Consultation with industry is underway to determine the feasibility.

Outcomes

The research will show: how physiological data can be used to visualise the efforts of staff to achieve their tasks; the events in rail signalling tasks that physiological measures can detect; whether physiological measures provide useful data to better understand aspects of task performance (e.g. attention, distraction, recovery from interruption); and perceptions and attitudes of staff surrounding the use of physiological measures. The knowledge gained from this, with consideration of the ethical issues surrounding using personal data, will have implications for industries beyond rail.

References

1. RSSB (2017) Rail technical strategy: Capability Delivery Plan. Available at: www.rssb.co.uk/rts/Documents/2017-01-27-rail-technical-strategy-capability-delivery-plan-brochure.pdf [Assessed 12.03.18]
2. Kyriakidis, M., Majumdar, A. and Ochieng, W.Y., 2015. Data based framework to identify the most significant performance shaping factors in railway operations. Safety science, 78, pp.60-76.
3. Pickup, L., Wilson, J.R., Norris, B.J., Mitchell, L. and Morrisroe, G., 2005. The Integrated Workload Scale (IWS): a new self-report tool to assess railway signaller workload. Applied Ergonomics, 36(6), pp.681-693.
4. Charles, R.L. and Nixon, J., 2019. Measuring mental workload using physiological measures: a systematic review. Applied ergonomics, 74, pp.221-232.
5. Gao, Q., Wang, Y., Song, F., Li, Z. and Dong, X., 2013. Mental workload measurement for emergency operating procedures in digital nuclear power plants. Ergonomics, 56(7), pp.1070-1085.
6. Lehrer, P., Karavidas, M., Lu, S.E., Vaschillo, E., Vaschillo, B. and Cheng, A., 2010. Cardiac data increase association between self-report and both expert ratings of task load and task performance in flight simulator tasks: An exploratory study. International Journal of Psychophysiology, 76(2), pp.80-87.
7. Nickel, P. and Nachreiner, F., 2003. Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload. Human factors, 45(4), pp.575-590.
8. Song, Y.S., Baek, J.H., Hwang, D. and Lee, J. (2014) Physiological Status Assessment of Locomotive Engineer During Train Operation. Journal of Electrical Engineering and Technology, 9(1), pp.324-333.
9. Collet, C., Salvia, E. and Petit-Boulanger, C. (2014) Measuring workload with electrodermal activity during common braking actions. Ergonomics, 57(6), pp.886–96.