Evaluation of ship navigator's mental workload using nasal temperature and heart rate variability

Murai, Koji; Hayashi, Yûji; Okazaki, Tadatsugi; Stone, Laurie; Mitomo, Nobuo

doi:10.1109/icsmc.2008.4811503

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…[28], this measure provides supplementary evidence to our fNIRS findings in favor of a deleterious mechanism on resource management induced by mental overload. Such a pattern of physiological response to mental overload has already been observed on other physiological responses such as pupil diameter[64][65], suggesting a change in ANS action to face mental overload.…”

supporting

confidence: 54%

“…One should also consider that the complexity of the link between physiological factors and mental workload, as higher task demands are not necessarily associated with a higher mental workload. Similar findings have also demonstrated that mental workload cannot be estimated precisely with the sole properties of the task because individual factors (e.g., expertise [34]) or environmental factors [28], will impact on the mental effort deployed to perform a given task. Consequently, mental workload should be defined in terms of the interaction between the task and the individual performing the task [72].…”

Section: Discussionmentioning

confidence: 56%

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Using near infrared spectroscopy and heart rate variability to detect mental overload

Durantin

Gagnon

Tremblay

et al. 2014

Behavioural Brain Research

218

158

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Mental workload is a key factor influencing the occurrence of human error, especially during piloting and remotely operated vehicle (ROV) operations, where safety depends on the ability of pilots to act appropriately. In particular, excessively high or low mental workload can lead operators to neglect critical information. The objective of the present study is to investigate the potential of functional near infrared spectroscopy (fNIRS) - a non-invasive method of measuring prefrontal cortex activity - in combination with measurements of heart rate variability (HRV), to predict mental workload during a simulated piloting task, with particular regard to task engagement and disengagement. Twelve volunteers performed a computer-based piloting task in which they were asked to follow a dynamic target with their aircraft, a task designed to replicate key cognitive demands associated with real life ROV operating tasks. In order to cover a wide range of mental workload levels, task difficulty was manipulated in terms of processing load and difficulty of control - two critical sources of workload associated with piloting and remotely operating a vehicle. Results show that both fNIRS and HRV are sensitive to different levels of mental workload; notably, lower prefrontal activation as well as a lower LF/HF ratio at the highest level of difficulty, suggest that these measures are suitable for mental overload detection. Moreover, these latter measurements point toward the existence of a quadratic model of mental workload.

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supporting

confidence: 54%

Section: Discussionmentioning

confidence: 56%

Using near infrared spectroscopy and heart rate variability to detect mental overload

Durantin

Gagnon

Tremblay

et al. 2014

Behavioural Brain Research

218

158

View full text Add to dashboard Cite

show abstract

“…In driving contexts, facial thermography was found to be useful in assessing over-arousal constructs such as mental workload (Or and Duffy, 2007 ; Murai et al, 2008 ). Performing a secondary workload task (mental arithmetic) while driving in a simulator as well as an on-road car led to a decrease in nasal temperature with stable forehead temperatures (Or and Duffy, 2007 ).…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

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

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

242

181

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As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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“…To do the measurement, we have the subject stand in front of the ship's compass which is exactly one meter from where the thermography device is set [12].…”

Section: Facial Temperaturementioning

confidence: 99%

Basic study of a mental workload for student's simulator training using heart rate variability, salivary amylase activity and facial temperature

Nishimura

Murai

Hayashi

2011

2011 6th International Conference on System of Systems Engineering

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Mental workload is useful to evaluate the performance of ship bridge teammates: a captain, a duty officer, a helmsman, and a pilot. The physiological indices, heart rate variability (R-R interval), salivary amylase and facial (nasal) temperature, are good indices for ship handling. It evaluated for seafarers on both of sim ulator and real ship; however, we need to evaluate using three indices simultaneously. This paper proposes that three physiological indices do well for evaluating student's mental workload for simulator training.

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Evaluation of ship navigator's mental workload using nasal temperature and heart rate variability

Cited by 20 publications

References 8 publications

Using near infrared spectroscopy and heart rate variability to detect mental overload

Using near infrared spectroscopy and heart rate variability to detect mental overload

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

Basic study of a mental workload for student's simulator training using heart rate variability, salivary amylase activity and facial temperature

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