Using Heart Rate Variability to Assess Operator Mental Workload in a Command and Control Simulation of Multiple Unmanned Aerial Vehicles

Abstract

Unmanned systems will play an increased role in the future beyond military application including but not limited to: search and rescue, border patrol, homeland security, and natural disaster relief operations. Current models of unmanned system operations, such as those used for unmanned aerial vehicles, require multiple operators to control a single vehicle. This multioperator-single vehicle ratio will soon shift to a multioperator-multivehicle model as the number of unmanned systems increase and work in unison to complete a mission. The purpose of this study was to determine the utility of a physiological measure i.e. heart rate variability (HRV), to assess operator workload in a single operator-multivehicle command and control simulation. An internally developed command and control simulator is described and observed effects of mental workload on HRV are reported. Results suggest that HRV can be used to assess operator workload during a command and control simulation of multiple unmanned aerial vehicles.

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

“…HRV has been shown to successfully capture changes in the sympathetic-parasympathetic balance and to be lowered by an increase in MW levels (Chaumet et al, 2019). In the context of controlling unmanned aerial vehicles, Jasper et al (2016) verified whether HRV could be used as a predictor of operator MW in this scenario. Each one of the 20 participants simultaneously controlled multiple vehicles while their ECG was monitored.…”

“…In case the operator needs to employ high levels of mental resources in order to achieve a required task performance for a long time, this might increase fatigue levels to such a point that the operator is no longer able to successfully perform the task. On the other hand, if the task is not demanding enough, it can lead to boredom and lack of engagement, which could also affect the operator's performance (Wilson and Russell, 2003a;Jasper et al, 2016). However, devising an objective strategy to assess MW is still an open challenge.…”

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

“…HRV also varies with workload experienced by drivers during simulated driving (Zhao et al, 2012 ; Heine et al, 2017 ; Hidalgo-Muñoz et al, 2018 ) and on-road driving (Lee et al, 2007 ). In addition, HRV variations due to cognitive workload have also been found in city traffic operators (Fallahi et al, 2016 ) and unmanned aerial vehicles operators (Jasper et al, 2016 ). HRV is sensitive to workload increases due to vigilance and situational awareness demands of the task (Saus et al, 2001 ; Stuiver et al, 2014 ; Jasper et al, 2016 ).…”

“…In addition, HRV variations due to cognitive workload have also been found in city traffic operators (Fallahi et al, 2016 ) and unmanned aerial vehicles operators (Jasper et al, 2016 ). HRV is sensitive to workload increases due to vigilance and situational awareness demands of the task (Saus et al, 2001 ; Stuiver et al, 2014 ; Jasper et al, 2016 ). However, at least one study (Shakouri et al, 2018 ) found no variation in heart rate variability metrics (RMSSD, LF, HF, and LF/HF ratio) as a function of higher traffic density while driving in a simulator, even though variations in subjective workload were found.…”

“…Similarly, other factors may impact HR/HRV, including task characteristics and motorists' state (relaxation, engagement, and motivation) and activities (smoking and posture). For instance, HRV may increase over time if the task becomes less difficult over time, which may put motorists in a more relaxed state (Jasper et al, 2016 ). Similarly, HRV may also increase over time with disengagement or demotivation to perform a difficult task (Jasper et al, 2016 ).…”