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

Hidalgo‐Muñoz, Antonio R.; Béquet, Adolphe J.; Astier-Juvenon, Mathis; Pépin, Guillaume; Fort, Alexandra; Jallais, Christophe; Tattegrain, Hélène; Gabaude, Catherine

doi:10.3389/fnhum.2018.00525

Cited by 32 publications

(36 citation statements)

References 45 publications

(54 reference statements)

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“…Respiration Features: From the respiration rate (RR) signal, arithmetic mean, standard deviation, and kurtosis are calculated as features in time domain. Similar to EEG, Welch’s method has been used to estimate the PSDs from the frequency ranges [0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.7], and [0.7, 1] [ 72 , 73 ]. The cognitive load has a distinct effect on the respiratory behaviour that can differ in sensitivity in the parameters obtained from respiratory signals [ 74 ].…”

Section: Methodsmentioning

confidence: 99%

“…The cognitive load has a distinct effect on the respiratory behaviour that can differ in sensitivity in the parameters obtained from respiratory signals [ 74 ]. According to Hidalgo-Muñoz et al [ 72 ] significant increases in the respiration rate are observed while driving in comparison to the base line condition. Moreover, the RR showed variations with a different level task difficulty and RR accelerated with an increasing cognitive workload.…”

Section: Methodsmentioning

confidence: 99%

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Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

Ahmed

Begum

2020

Brain Sciences

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One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

Ahmed

Begum

2020

Brain Sciences

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“…Interval durations shorter than 0.5 s were considered as artifacts and removed. From the Fourier power spectrum of the filtered signal, the integrals in the frequency bands from 0.07 to 0.14 and 0.15 to 0.5 Hz, normalized by the total power, yielded spectral power features in the mid and high frequency bands, respectively (Hidalgo-Muñoz et al, 2018 ). RR parameters are listed in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

Subjective Evaluation of Performance in a Collaborative Task Is Better Predicted From Autonomic Response Than From True Achievements

Maÿe

Lorenz²,

Stoica

et al. 2020

Front. Hum. Neurosci.

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Whereas the fundamental role of the body in social cognition seems to be generally accepted, elucidating the bodily mechanisms associated with non-verbal communication and cooperation between two or more persons is still a challenging endeavor. In this article we propose a fresh approach for investigating the function of the autonomic nervous system that is reflected in parameters of heart rate variability, respiration, and electrodermal activity in a social setting. We analyzed autonomic parameters of dyads solving a target-tracking task together with the partner or individually. A machine classifier was trained to predict the subjects' rating of performance and collaboration either from tracking error data or from the set of autonomic parameters. When subjects collaborated, this classifier could predict the subjective performance ratings better from the autonomic response than from the objective performance of the subjects. However, when they solved the task individually, predictability from autonomic parameters dropped to the level of objective performance, indicating that subjects were more rational in rating their performance in this condition. Moreover, the model captured general knowledge about the population that allows it to predict the performance ratings of an unseen subject significantly better than chance. Our results suggest that, in particular in situations that require collaboration with others, evaluation of performance is shaped by the bodily processes that are quantified by autonomic parameters. Therefore, subjective performance assessments appear to be modulated not only by the output of a rational or discriminative system that tracks the objective performance but to a significant extent also by interoceptive processes.

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“…1C). Initially, it was hypothesised that the higher HR response may have been the consequence of increased cognitive demand due to dual-tasking, 31 as participants were interpreting real-time information while maintaining their cycling work-rate. However, when HR responses were expressed relative to participants' maximal HR, these betweencondition differences were not observed.…”

Section: Discussionmentioning

confidence: 99%

The availability of task-specific feedback does not affect 20 km time trial cycling performance or test-retest reliability in trained cyclists

Borg

Osborne

Stewart

et al. 2020

Journal of Science and Medicine in Sport

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Objectives: This study examined the influence of the availability of task-specific feedback on 20 km time trial (20TT) cycling performance and test-retest reliability. Design: Thirty trained, club-level cyclists completed two 20TT's on different days, with (feedback, FB) or without (no-feedback, NFB) task-specific feedback (i.e., power output, cadence, gear and heart rate [HR]). Elapsed distance was provided in both conditions. Methods: During trials, ergometer variables and HR were continuously recorded, and a rating of perceived exertion (RPE) was collected every 2 km. Data were analysed using linear mixed-effects models in a Bayesian framework, and Cohen's d was calculated for standardised differences. The reliability of finish time and mean power output (PO) was determined via multiple indices, including intraclass correlations (ICC). Results: Performance, pacing behaviour, and RPE were not statistically different between conditions. The posterior mean difference [95% credible interval] between TT1 and TT2 for FB and NFB was 10 s [−5, 25] and −2 s [−17, 14], respectively. In TT2, HR was statistically higher (∼8 b min −1) in FB compared to NFB after 13 km (d = 2.08-2.25). However, this result was explained by differences in maximal HR. Finish time (FB: ICC = 0.99; NFB: ICC = 0.99) and mean power output (FB: ICC = 0.99; NFB: ICC = 0.99) in each condition were substantially reliable. Conclusions: The availability of task-specific information did not affect 20TT performance or reliability. Except for elapsed distance, task-specific feedback should be withheld from trained cyclists when evaluating interventions that may affect performance, to prevent participants from recalling previous performance settings.

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

Cited by 32 publications

References 45 publications

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

Subjective Evaluation of Performance in a Collaborative Task Is Better Predicted From Autonomic Response Than From True Achievements

The availability of task-specific feedback does not affect 20 km time trial cycling performance or test-retest reliability in trained cyclists

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