The role of respiratory measures to assess mental load in pilot selection

Classical Music Students’ Pre-performance Anxiety, Catastrophizing, and Bodily Complaints Vary by Age, Gender, and Instrument and Predict Self-Rated Performance Quality

Music performance anxiety (MPA) is a multifaceted phenomenon occurring on a continuum of severity. In this survey study, we investigated to what extent the affective (anxiety), cognitive (catastrophizing), and somatic (bodily complaints) components of MPA prior to solo performances vary as a function of age, gender, instrument group, musical experience, and practice as well as how these MPA components relate to self-rated change in performance quality from practice to public performance. The sample comprised 75 male and 111 female classical music university students, aged 15–45 years. Age was positively associated with anxious feelings and bodily complaints. Compared to male students, female students reported significantly more anxious feelings and catastrophizing. Singers reported less anxious feelings and catastrophizing than instrumentalists. Breathing-, mouth- and throat-related complaints were highest among singers and wind players; hand- and arm-related complaints were highest among string players and pianists. The indices of musical experience and practice had marginal effects. An average of four bodily complaints bothered the participants strongly to very strongly. Worsening in performance quality from practice to public performance was reported by almost half of the participants and was best predicted by anxious feelings and breathing-related complaints. We conclude that age, gender and instrument play a significant role in understanding the phenomenology of MPA. Musicians should be examined according to these characteristics rather than as one homogenous population. In particular, it might be valuable to develop assessment tools for MPA that incorporate items related to the bodily complaints that are most relevant to the different instrument groups. Breathing-related complaints could add an important dimension to the investigation of MPA and music performance. Finally, the high percentage of students reporting worsening of their performance quality from practice to public performance highlights the need of professional support to help music students be able to perform at their best and thrive as artists.

Subjective and objective assessments of mental workload for UAV operations

BACKGROUND

Unmanned Aerial Vehicles (UAVs) have created safety problems for the publics. Assessments of the mental workload for UAV operations are essential to realize the causes of UAV accidents.

OBJECTIVE

To test the following hypotheses: i. mission difficulty in UAV operation affects both subjective and objective measures of mental workload; ii. mission difficulty affects number of failures in UAV operation.

METHODS

Fourteen male adults participated as UAV operators after attending a UAV training course. They performed four flight missions of different difficulty levels. During their flight missions, their heart rate and inter-beat interval (IBI) were collected. Upon completing each flight mission, the participants gave subjective ratings of mental workload using three commonly adopted assessment tools. The time of flight and number of failures in flight operations were also recorded.

RESULTS

The results showed that mission difficulty affected the scores of all three assessment tools significantly. Mission difficulty also affected number of failures and IBI significantly. The scores of the three assessment tools were highly correlated (ρ= 0.7 to 0.83, p < 0.001) with one another. The results of the three subjective ratings were also consistent with that of the IBI data.

CONCLUSIONS

High mental workload in UAV operation could lead to poor flight performance.

: An R Package for performing kernel change point detection on the running statistics of multivariate time series

In many scientific disciplines, researchers are interested in discovering when complex systems such as stock markets, the weather or the human body display abrupt changes. Essentially, this often comes down to detecting whether a multivariate time series contains abrupt changes in one or more statistics, such as means, variances or pairwise correlations. To assist researchers in this endeavor, this paper presents the package for performing kernel change point (KCP) detection on user-selected running statistics of multivariate time series. The running statistics are extracted by sliding a window across the time series and computing the value of the statistic(s) of interest in each window. Next, the similarities of the running values are assessed using a Gaussian kernel, and change points that segment the time series into maximally homogeneous phases are located by minimizing a within-phase variance criterion. To decide on the number of change points, a combination of a permutation-based significance test and a grid search is provided. stands out among the variety of change point detection packages available in because it can be easily adapted to uncover changes in any user-selected statistic without imposing any distribution on the data. To exhibit the usefulness of the package, two empirical examples are provided pertaining to two types of physiological data.

The Validity of Physiological Measures to Identify Differences in Intrinsic Cognitive Load

A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016-2020) that used physiological measures to measure intrinsic cognitive load. Only studies that required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.

Breathing variability-implications for anaesthesiology and intensive care

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

Prediction of Stress and Mental Workload during Police Academy Training Using Ultra-Short-Term Heart Rate Variability and Breathing Analysis

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Such constructs are crucial in assessing quality-of-life of individuals, as well as their overall performance when doing critical tasks. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings remains unknown. Additionally, it is desirable for such mental state monitoring systems to have high temporal resolution, thus allowing for quick feedback and adaptive decision making. In this article, we explore the use of features computed from time windows much shorter than typically reported in the literature. More specifically, we evaluate the potential of HRV and breathing features computed over so-called ultra-short-term segments (i.e., < 5 minutes) for stress and mental workload prediction. Experiments with 27 police academy trainees show that short time windows as low as 60 seconds can provide useful insights, in particular for mental workload assessment. Moreover, the fusion of HRV and breathing features showed to be an important aspect for reliable behavioural assessment in highly ecological settings.

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.

Respiratory Variability, Sighing, Anxiety, and Breathing Symptoms in Low- and High-Anxious Music Students Before and After Performing

Music performance anxiety (MPA) is a major problem for music students. It is largely unknown whether music students who experience high or low anxiety differ in their respiratory responses to performance situations and whether these co-vary with self-reported anxiety, tension, and breathing symptoms. Affective processes influence dynamic respiratory regulation in ways that are reflected in measures of respiratory variability and sighing. This study had two goals. First, we determined how measures of respiratory variability, sighing, self-reported anxiety, tension, and breathing symptoms vary as a function of the performance situation (practice vs. public performance), performance phase (pre-performance vs. post-performance), and the general MPA level of music students. Second, we analyzed to what extent self-reported anxiety, tension, and breathing symptoms co-vary with the respiratory responses. The participants were 65 university music students. We assessed their anxiety, tension, and breathing symptoms with Likert scales and recorded their respiration with the LifeShirt system during a practice performance and a public performance. For the 10-min periods before and after each performance, we computed number of sighs, coefficients of variation (CVs, a measure of total variability), autocorrelations at one breath lag (ARs(1), a measure of non-random variability) and means of minute ventilation (V’_E), tidal volume (V_T), inspiration time (T_I), and expiration time (T_E). CVs and sighing were greater whereas AR(1) of V’_E was lower in the public session than in the practice session. The effect of the performance situation on CVs and sighing was larger for high-MPA than for low-MPA participants. Higher MPA levels were associated with lower CVs. At the within-individual level, anxiety, tension, and breathing symptoms were associated with deeper and slower breathing, greater CVs, lower AR(1) of V’_E, and more sighing. We conclude that respiratory variability and sighing are sensitive to the performance situation and to musicians’ general MPA level. Moreover, anxiety, tension, breathing symptoms, and respiratory responses co-vary significantly in the context of music performance situations. Respiratory monitoring can add an important dimension to the understanding of music performance situations and MPA and to the diagnostic and intervention outcome assessments of MPA.

Effect of Mental Workload on Breathing Pattern and Heart Rate for a Working Memory Task: A Pilot Study

Mental workload or cognitive load is the total amount of mental resources required while doing a task. Apart from qualitative measures, various physiological signals are being used for assessment of mental workload. However, very limited research has been done on assessment of cognitive load from respiratory signals. In the present study, we have tried to analyze the cognitive load mainly based on respiratory features. n-back memory test has been modified to impart low and high cognitive load. The peripheral blood volume signal (PPG) collected while executing the task is used to reconstruct the breathing pattern signal. A number of morphological as well as statistical features are calculated from this reconstructed signal. Finally a classifier is used for classifying the low and high cognitive load. Results show that a classification accuracy of 76.8% is obtained while using respiratory features only. A maximum accuracy of 81.80% is obtained if we combine time domain PPG features with respiratory features. The features finally selected can also be used to study the habituation effect.

A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively.

Contact-Based Methods for Measuring Respiratory Rate

There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject's body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

Individual differences in cardiorespiratory measures of mental workload: An investigation of negative affectivity and cognitive avoidant coping in pilot candidates

Cardiorespiratory measures provide useful information in addition to well-established self-report measures when monitoring operator capacity. The purpose of our study was to refine the assessment of operator load by considering individual differences in personality and their associations with cardiorespiratory activation. Physiological and self-report measures were analyzed in 115 pilot candidates at rest and while performing a multiple task covering perceptual speed, spatial orientation, and working memory. In the total sample and particularly in individuals with a general tendency to worry a lot, a cognitive avoidant coping style was associated with a smaller task-related increase in heart rate. Negative affectivity was found to moderate the association between cardiac and self-reported arousal. Given that physiological and self-report measures of mental workload are usually combined when evaluating operator load (e.g., in pilot selection and training), our findings suggest that integrating individual differences may reduce unexplained variance and increase the validity of workload assessments.

Respiratory Changes in Response to Cognitive Load: A Systematic Review

When people focus attention or carry out a demanding task, their breathing changes. But which parameters of respiration vary exactly and can respiration reliably be used as an index of cognitive load? These questions are addressed in the present systematic review of empirical studies investigating respiratory behavior in response to cognitive load. Most reviewed studies were restricted to time and volume parameters while less established, yet meaningful parameters such as respiratory variability have rarely been investigated. The available results show that respiratory behavior generally reflects cognitive processing and that distinct parameters differ in sensitivity: While mentally demanding episodes are clearly marked by faster breathing and higher minute ventilation, respiratory amplitude appears to remain rather stable. The present findings further indicate that total variability in respiratory rate is not systematically affected by cognitive load whereas the correlated fraction decreases. In addition, we found that cognitive load may lead to overbreathing as indicated by decreased end-tidal CO₂ but is also accompanied by elevated oxygen consumption and CO₂ release. However, additional research is needed to validate the findings on respiratory variability and gas exchange measures. We conclude by outlining recommendations for future research to increase the current understanding of respiration under cognitive load.

A linear model for event-related respiration responses

BACKGROUND

Cognitive processes influence respiratory physiology. This may allow inferring cognitive states from measured respiration. Here, we take a first step towards this goal and investigate whether event-related respiratory responses can be identified, and whether they are accessible to a model-based approach.

NEW METHOD

We regard respiratory responses as the output of a linear time invariant system that receives brief inputs after psychological events. We derive average responses to visual targets, aversive stimulation, and viewing of arousing pictures, in interpolated respiration period (RP), respiration amplitude (RA), and respiratory flow rate (RFR). We then base a Psychophysiological Model (PsPM) on these averaged event-related responses. The PsPM is inverted to yield estimates of cognitive input into the respiratory system. This method is validated in an independent data set.

RESULTS

All three measures show event-related responses, which are captured as non-zero response amplitudes in the PsPM. Amplitude estimates for RA and RFR distinguish between picture viewing and the other tasks. This pattern is replicated in the validation experiment.

COMPARISON WITH EXISTING METHODS

Existing respiratory measures are based on relatively short time-intervals after an event while the new method is based on the entire duration of respiratory responses.

CONCLUSION

Our findings suggest that interpolated respiratory measures show replicable event-related response patterns. PsPM inversion is a suitable approach to analysing these patterns, with a potential to infer cognitive processes from respiration.