Assessment and monitoring of mental workload in subway train operations using physiological, subjective, and performance measures

Abstract

Subway train operation is a complex, sociotechnical system that involves a variety of cognitively demanding tasks. The train operators are responsible for continuously monitoring the surrounding environment, maintaining awareness, processing information, and making decisions under risk. The resulting mental strain on operators can negatively affect their performance and the interaction of the human-machine system. The objective of this study was to evaluate if physiological, subjective, and performance measures could identify the level of mental workloads arising from routine and nonroutine operations in the subway system. A total of 11 subway train operators underwent different driving scenarios in a high-fidelity simulator. The simulated tasks were divided into two categories: routine operations (preparing to drive and driving between stations without interruptions or emergencies) and nonroutine operation (responding to a tunnel fire, dealing with a high density of passengers, encountering a passenger/technician on the track, and responding to train failure). The mental workload was monitored and evaluated in these tasks using an electrocardiogram, subjective self-rating scales, and driving performance. Both heart rate variability and performance measures (including reaction time and error rate) detected mental workload variations in the different operations. On the other hand, the subjective ratings (including NASA-TLX) assessed the overall mental workload associated with a task, without explaining the mental demand variations within the task over time. Subway train drivers experienced different levels of mental workload during routine and nonroutine driving conditions.

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

“…Cognitive load has been widely analyzed in the study of an individual’s workload during a driving task. In this context, physiological signals have been considered to monitor the level of driver physiological arousal in response to cognitive load during car driving [ 27 , 38 ], to evaluate the level of mental workloads in subway driver operators [ 39 ] or to assess the mental workload in an aircraft pilot during flights [ 40 , 41 ].…”

Pattern analysis of physiological data for the assessment of mental workload

“…All those methods have in common that they assume to be able to record the individual workers changes of MWL at the work place. A wide range of studies was conducted proving in either laboratory or field settings that those indicators are able to differentiate between levels of MWL (Myrtek et al 1994;Marquart et al 2015;Delliaux et al 2019;Jafari et al 2020), assembly products of different complexity Bornewasser 2020, 2021), or even to detect potential mental overload (Hoover et al 2012). Regarding the measurement of MWL the most discussable points seem to be which indicator to choose, how to analyze it, and if all those indicators are measuring the same aspect of MWL or if it is a rather multidimensional construct (Matthews et al 2015a).…”

Detection of the Driver’s Mental Workload Level in Smart and Autonomous Systems Using Physiological Signals

“…e simulation tasks are separated into two groups: conventional operation (preparation for driving and driving without interruption or emergency) and unconventional operation (dealing with tunnel fires, handling high-density passengers, meeting passengers/technicians on track, and dealing with train failures). Using electrophoresis to monitor and evaluate mental workloads in these tasks [13]. When the driver is under a situation of a high or low level of mental workload for a long time, the chance of driving fatigue state will be increased.…”