Preliminary Results of the LF/HF Ratio as an Indicator for Estimating Difficulty Level of Flight Tasks

Skibniewski, Franciszek; Dziuda, Łukasz; Baran, Paulina M; Krej, Mariusz; Guzowski, Sylwester; Piotrowski, Marcin; Truszczyński, Olaf

doi:10.3357/amhp.4087.2015

Cited by 22 publications

(17 citation statements)

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“…Regarding the spectral analysis, in our experiment, the increase of LF/HF ratio is in accordance with the results obtained with pilots during assisted and unassisted flight, where arousal is modulated by the flight task complexity (Skibniewski et al, 2015). According to our results, the LF/HF increase is due to the HF decrease (diminution of the parasympathetic influence).…”

Section: Discussionsupporting

confidence: 90%

Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving

Hidalgo‐Muñoz

Béquet

Astier-Juvenon

et al. 2019

Front. Hum. Neurosci.

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Research works on operator monitoring underline the benefit of taking into consideration several signal modalities to improve accuracy for an objective mental state diagnosis. Heart rate (HR) is one of the most utilized systemic measures to assess cognitive workload (CW), whereas, respiration parameters are hardly utilized. This study aims at verifying the contribution of analyzing respiratory signals to extract features to evaluate driver’s activity and CW variations in driving. Eighteen subjects participated in the study. The participants carried out two different cognitive tasks requiring different CW demands, a single task as well as a competing cognitive task realized while driving in a simulator. Our results confirm that both HR and breathing rate (BR) increase in driving and are sensitive to CW. However, HR and BR are differently modulated by the CW variations in driving. Specifically, HR is affected by both driving activity and CW, whereas, BR is suitable to evidence a variation of CW only when driving is not required. On the other hand, spectral features characterizing respiratory signal could be also used similarly to HR variability indices to detect high CW episodes. These results hint the use of respiration as an alternative to HR to monitor the driver mental state in autonomic vehicles in order to predict the available cognitive resources if the user has to take over the vehicle.

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Section: Discussionsupporting

confidence: 90%

Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving

Hidalgo‐Muñoz

Béquet

Astier-Juvenon

et al. 2019

Front. Hum. Neurosci.

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“…HRV and cognition background HRV has been found to be reactive to mental workload and stress [ 15 ] in multiple contexts, e.g., various tests requiring executive functions [ 16 ], during aircraft piloting [ 17 ], and in computer work [ 18 , 19 ]. Among others, Thayer et al [ 20 ] and Lane et al [ 21 ] have described integrative models of neural structure and autonomic regulation, and presented supporting studies which link cognition and HRV.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Collaboration Found in Cardiac Physiology: Study in Classroom Environment

et al. 2016

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It is known that periods of intense social interaction result in shared patterns in collaborators’ physiological signals. However, applied quantitative research on collaboration is hindered due to scarcity of objective metrics of teamwork effectiveness. Indeed, especially in the domain of productive, ecologically-valid activity such as programming, there is a lack of evidence for the most effective, affordable and reliable measures of collaboration quality. In this study we investigate synchrony in physiological signals between collaborating computer science students performing pair-programming exercises in a class room environment. We recorded electrocardiography over the course of a 60 minute programming session, using lightweight physiological sensors. We employ correlation of heart-rate variability features to study social psychophysiological compliance of the collaborating students. We found evident physiological compliance in collaborating dyads’ heart-rate variability signals. Furthermore, dyads’ self-reported workload was associated with the physiological compliance. Our results show viability of a novel approach to field measurement using lightweight devices in an uncontrolled environment, and suggest that self-reported collaboration quality can be assessed via physiological signals.

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“…In more advanced tasks like driving, some investigators similarly showed that SDNN failed to differentiate between varying difficulty levels during n-back memory recall tasks (Mehler et al 2011); however, Mehler et al (2011) did suggest that other time-domain HRV metrics (e.g., RMSSD) may be more useful in cognitive demand during driving, concluding that physiological measures need to be selected carefully depending on what aspects of workload are of interest. Specific to aviation, several studies focused on flight tasks and flight simulation (Dussault et al 2004;Dussault et al 2005;Skibniewski et al 2015;Wei et al 2014a) and concluded that HRV was not as sensitive to varying cognitive demands as other physiological signals (G. F. Wilson 2002). From the systematic review (Charles and Nixon 2019), the general conclusion on HR and HRV was that their ability to distinguish mental workload was influenced by application domain, task duration, and task factors.…”

Section: Physiological Signals At the Scenario Levelmentioning

confidence: 99%

Multi-modal physiological sensing approach for distinguishing high workload events in remotely piloted aircraft simulation

Antonik

Webber

et al. 2019

Hum.-Intell. Syst. Integr.

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Preliminary Results of the LF/HF Ratio as an Indicator for Estimating Difficulty Level of Flight Tasks

Cited by 22 publications

References 0 publications

Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving

Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving

Cognitive Collaboration Found in Cardiac Physiology: Study in Classroom Environment

Multi-modal physiological sensing approach for distinguishing high workload events in remotely piloted aircraft simulation

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