Mental Workload Alters Heart Rate Variability, Lowering Non-linear Dynamics

Engagement of mental effort in response to mental fatigue: A psychophysiological analysis

Acute mental fatigue, characterized by a transient decline in cognitive efficiency during or following prolonged cognitive tasks, can be managed through adaptive effort deployment. In response to mental fatigue, individuals can employ two main behavioral patterns: engaging a compensatory effort to limit performance decrements, or disengaging effort, leading to performance deterioration. This study investigated the behavioral pattern used by participants in mental fatigue conditions. Fifty participants underwent a sequential-task protocol with counterbalanced sessions who took place in two separate sessions: a 30-min incongruent Stroop task (fatiguing session) or a 30-min documentary viewing task (control session), followed by a time-to-exhaustion (TTE) handgrip task at 13 % of maximal voluntary contraction. Psychophysiological measures included the preejection period, heart rate variability, blood pressure, and respiration. Behavioral results showed deteriorated TTE handgrip performance after the Stroop task compared to after the documentary viewing task. During the Stroop task participants were more conservative and prioritized accuracy over speed. Self-reported fatigue was greater after the Stroop task. Psychophysiological data revealed a gradual decrease in sympathetic activity over time in both tasks, with the Stroop task showing a more pronounced decrease. Taken together, these findings suggest a disengagement of effort for a large proportion of participants (49 %) that could be partly attributed to a habituation to the demands of the Stroop task. This study illustrates the interplay of behavioral patterns of effort investment in the context of mental fatigue and underscores the role of disengagement as a dominant response to this phenomenon among healthy participants.

A comprehensive evaluation of linear and non-linear HRV parameters between paced breathing and stressful mental state

Background

Heart rate variability (HRV) is a crucial metric that provides valuable insight into the balance between relaxation and stress. Previous research has shown that most HRV parameters improve during periods of mental relaxation, while decreasing during tasks involving cognitive workload. Although a comprehensive analysis of both linear and non-linear HRV parameters has been carried out in existing literature, there still exists a need for further research in this area. Additionally, limited knowledge exists regarding how specific interventions may influence the interpretation of these parameters and how the different parameters correlate under different interventions. This study aims to address these gaps by conducting a thorough comparison of different linear and non-linear HRV parameters under mentally relaxed versus stressful states.

Methodology

Participants were randomly and equally divided among two between-subjects groups: relaxed-stress (RS) (N = 22) and stress-relaxed (SR) (N = 22). In the RS group, a paced breathing task was given for 5 min to create relaxation, and was followed by a 5-min time-based mental calculation task to create stress. In the SR group, the order of the stress and relaxed tasks was reversed. There was a washout period of 15 min after the first task in both groups.

Results

Of the 37 HRV parameters, 33 differed significantly between the two interventions. The majority of the parameters exhibited an improving and degrading tendency of HRV parameters in the relaxed and stressed states, respectively. The correlation of the majority of HRV parameters decreases during stress, while prominent time domain and geometric domain parameters stand out in the correlation.

Conclusion

Overall, HRV parameters can be reliably used to assess a person's relaxed and stressed mental states during paced breathing and mental arithmetic task respectively. Furthermore, non-linear HRV parameters provide accurate estimators of the mental state, in addition to the commonly used linear parameters.

Heart rate variability helps classify phenotype in systemic sclerosis

We aimed to develop a systemic sclerosis (SSc) subtypes classifier tool to be used at the patient's bedside. We compared the heart rate variability (HRV) at rest (5-min) and in response to orthostatism (5-min) of patients (n = 58) having diffuse (n = 16, dcSSc) and limited (n = 38, lcSSc) cutaneous forms. The HRV was evaluated from the beat-to-beat RR intervals in time-, frequency-, and nonlinear-domains. The dcSSc group differed from the lcSSc group mainly by a higher heart rate (HR) and a lower HRV, in decubitus and orthostatism conditions. Stand-up maneuver lowered HR standard deviation (sd_HR), the major axis length of the fitted ellipse of Poincaré plot of RR intervals (SD2), and the correlation dimension (CorDim) in the dcSSc group while increased these HRV indexes in the lcSSc group (p = 0.004, p = 0.002, and p = 0.004, respectively). We identified the 5 most informative and discriminant HRV variables. We then compared 341 classifying models (1 to 5 variables combinations × 11 classifier algorithms) according to mean squared error, logloss, sensitivity, specificity, precision, accuracy, area under curve of the ROC-curves and F1-score. F1-score ranged from 0.823 for the best 1-variable model to a maximum of 0.947 for the 4-variables best model. Most specific and precise models included sd_HR, SD2, and CorDim. In conclusion, we provided high performance classifying models able to distinguish diffuse from limited cutaneous SSc subtypes easy to perform at the bedside from ECG recording. Models were based on 1 to 5 HRV indexes used as nonlinear markers of autonomic integrated influences on cardiac activity.

Onge P, McDonald SM, Temme LA. Examining the Influence of Cognitive Load and Environmental Conditions on Autonomic Nervous System Response in Military Aircrew: A Hypoxia-Normoxia Study

Executing flight operations demand that military personnel continuously perform tasks that utilize low- and high-order cognitive functions. The autonomic nervous system (ANS) is crucial for regulating the supply of oxygen (O2) to the brain, but it is unclear how sustained cognitive loads of different complexities may affect this regulation. Therefore, in the current study, ANS responses to low and high cognitive loads in hypoxic and normoxic conditions were evaluated. The present analysis used data from a previously conducted, two-factor experimental design. Healthy subjects (n = 24) aged 19 to 45 years and located near Fort Novosel, AL, participated in the parent study. Over two, 2-h trials, subjects were exposed to hypoxic (14.0% O2) and normoxic (21.0% O2) air while simultaneously performing one, 15-min and one, 10-min simulation incorporating low- and high-cognitive aviation-related tasks, respectively. The tests were alternated across five, 27-min epochs; however, only epochs 2 through 4 were used in the analyses. Heart rate (HR), HR variability (HRV), and arterial O2 saturation were continuously measured using the Warfighter MonitorTM (Tiger Tech Solutions, Inc., Miami, FL, USA), a previously validated armband device equipped with electrocardiographic and pulse oximetry capabilities. Analysis of variance (ANOVA) regression models were performed to compare ANS responses between the low- and high-cognitive-load assessments under hypoxic and normoxic conditions. Pairwise comparisons corrected for familywise error were performed using Tukey's test within and between high and low cognitive loads under each environmental condition. Across epochs 2 through 4, in both the hypoxic condition and the normoxic condition, the high-cognitive-load assessment (MATB-II) elicited heightened ANS activity, reflected by increased HR (+2.4 ± 6.9 bpm) and decreased HRV (-rMSSD: -0.4 ± 2.7 ms and SDNN: -13.6 ± 14.6 ms). Conversely, low cognitive load (ADVT) induced an improvement in ANS activity, with reduced HR (-2.6 ± 6.3 bpm) and increased HRV (rMSSD: +1.8 ± 6.0 ms and SDNN: vs. +0.7 ± 6.3 ms). Similar observations were found for the normoxic condition, albeit to a lower degree. These within-group ANS responses were significantly different between high and low cognitive loads (HR: +5.0 bpm, 95% CI: 2.1, 7.9, p < 0.0001; rMSSD: -2.2 ms, 95% CI: -4.2, -0.2, p = 0.03; SDNN: -14.3 ms, 95% CI: -18.4, -10.1, p < 0.0001) under the hypoxic condition. For normoxia, significant differences in ANS response were only observed for HR (+4.3 bpm, 95% CI: 1.2, 7.4, p = 0.002). Lastly, only high cognitive loads elicited significant differences between hypoxic and normoxic conditions but just for SDNN (-13.3 ms, 95% CI, -17.5, -8.9, p < 0.0001). Our study observations suggest that compared to low cognitive loads, performing high-cognitive-load tasks significantly alters ANS activity, especially under hypoxic conditions. Accounting for this response is critical, as military personnel during flight operations sustain exposure to high cognitive loads of unpredictable duration and frequency. Additionally, this is likely compounded by the increased ANS activity consequent to pre-flight activities and anticipation of combat-related outcomes.

Cardiac indices of driver fatigue across in-lab and on-road studies

Driver fatigue is a major contributor to road accidents. Therefore, driver assistance systems (DAS) that would monitor drivers' states may contribute to road safety. Such monitoring can potentially be achieved with input from ECG indices (e.g., heart rate). We reviewed the empirical literature on responses of cardiac measures to driver fatigue and on detecting fatigue with cardiac indices and classification algorithms. We used meta-analytical methods to explore the pooled effect sizes of different cardiac indices of fatigue, their heterogeneity, and the consistency of their responses across studies. Our large pool of studies (N = 39) allowed us to stratify the results across on-road and simulator studies. We found that despite the large heterogeneity of the effect sizes between the studies, many indices had significant pooled effect sizes across the studies, and more frequently across the on-road studies. We also found that most indices showed consistent responses across both on-road and simulator studies. Regarding the detection accuracy, we found that even on-road classification could have been as accurate as 70% with only 2-min of data. However, we could only find two on-road studies that employed fatigue classification algorithms. Overall, our findings are encouraging with respect to the prospect of using cardiac measures for detecting driver fatigue. Yet, to fully explore this possibility, there is a need for additional on-road studies that would employ a similar set of cardiac indices and detection algorithms, a unified definition of fatigue, and additional levels of fatigue than the two fatigue vs alert states.

Change in heart rate variability with increasing time-on-task as a marker for mental fatigue: A systematic review

Fatigue-specific changes in the autonomic nervous system are often assumed to underlie the development of mental fatigue caused by prolonged cognitive tasks (i.e. Time-on-Task). Therefore, several previous studies have chosen to investigate the Time-on-Task related changes in heart rate variability (HRV). However, previous studies have used many different HRV indices, and their results often show inconsistencies. The present study, therefore, systematically reviewed previous empirical HRV studies with healthy individuals and in which mental fatigue is induced by prolonged cognitive tasks. Articles relevant to the objectives were systematically searched and selected by applying the PRISMA guidelines. We screened 360 records found on 4 databases and found that 19 studies were eligible for full review in accordance with the inclusion criteria. In general, all studies reviewed (with the exception of two studies) found significant changes in HRV with increasing Time-on-Task, suggesting that HRV is a reliable autonomic marker for Time-on-Task induced fatigue. The most conclusive HRV indices that showed a consistent Time-on-Task effect were the low frequency component of HRV and the time domain indices, particularly the root mean square of successive differences. Time-on-Task typically induced an increasing trend in both type of measures.

Physiological Indicators of Fluency and Engagement during Sequential and Simultaneous Modes of Human-Robot Collaboration

OCCUPATIONAL APPLICATIONSAn understanding of fluency in human-robot teaming from a physiological standpoint is still incomplete. In our experimental study involving 24 participants, we designed a scenario for shared-space human-robot collaboration (HRC) for a material sorting task. When compared to a sequential mode of interaction, the simultaneous mode resulted in significantly higher perceptions of fluency and engagement, primarily by reducing human idle time. These observations were complemented by significant changes in physiological responses, such as ECG entropy and low frequency power. These responses could predict fluency and engagement with accuracies of 90 and 97%, respectively. Notably, the perception of fluency and preferred mode of interaction were influenced by individual preferences. Hence, it is crucial to consider both physiological responses and user preferences when designing HRC systems, to ensure a positive experience with the robot teammate and to foster engagement in long-term teamwork. Furthermore, these signals can be obtained using a single robust, low-cost, and comfortable sensor.

Acute Effects of Fatigue on Cardiac Autonomic Nervous Activity

The onset of fatigue disrupts the functioning of the autonomic nervous system (ANS), potentially elevating the risk of life-threatening incidents and impairing daily performance. Previous studies mainly focused on physical fatigue (PF) and mental fatigue (MF) effects on the ANS, with limited knowledge concerning the influence of physical-mental fatigue (PMF) on ANS functionality. This study aimed to assess the immediate impact of PMF on ANS function and to compare its effects with those of PF and MF on ANS function. Thirty-six physically active college students (17 females) without burnout performed 60-min cycling exercises, AX-Continuous Performance Task (AX-CPT), and cycling combined with AX-CPT to induce PF, MF, and PMF respectively. Subjective fatigue levels were measured using the Rating of Perceived Exertion scale and the Visual Analog Scale-Fatigue. Heart rate variability was measured before and after each protocol to assess cardiac autonomic function. The proposed tasks successfully induced PF, MF, and PMF, demonstrated by significant changes in subjective fatigue levels. Compared with baseline, PMF decreased the root mean square of successive differences (RMSSD) between normal heartbeats (P < 0.001, d = 0.50), the standard deviation of normal-to-normal RR intervals (SDNN) (P < 0.01, d = 0.33), and the normalized high-frequency (nHF) power (P < 0.001, d = 0.32) while increased the normalized low-frequency (nLF) power (P < 0.001, d = 0.35) and the nLF/nHF ratio (P < 0.001, d = 0.40). Compared with MF, PMF significantly decreased RMSSD (P < 0.001, η2 = 0.431), SDNN (P < 0.001, η2 = 0.327), nLF (P < 0.01, η2 = 0.201), and nHF (P < 0.001, η2 = 0.377) but not the nLF/nHF ratio. There were no significant differences in ΔHRV (i.e., ΔRMSSD, ΔSDNN, ΔnLF/nHF, ΔnLF, and ΔnHF), heart rate, and training impulse between PF- and PMF-inducing protocols. Cognitive performance (i.e., accuracy) in AX-CPT during the PMF-inducing protocol was significantly lower than that during the MF-inducing protocol (P < 0.001, η2 = 0.101). PF and PMF increased sympathetic activity and decreased parasympathetic activity, while MF enhanced parasympathetic activity.

Adaptive Human-Robotic Interaction for Robotic-assisted Surgical Settings

INTRODUCTION

Increased complexity in robotic-assisted surgical system interfaces introduces problems with human-robot collaboration that result in excessive mental workload (MWL), adversely impacting a surgeon's task performance and increasing error probability. Real-time monitoring of the operator's MWL will aid in identifying when and how interventions can be best provided to moderate MWL. In this study, an MWL-based adaptive automation system is constructed and evaluated for its effectiveness during robotic-assisted surgery.

MATERIALS AND METHODS

This study recruited 10 participants first to perform surgical tasks under different cognitive workload levels. Physiological signals were obtained and employed to build a real-time system for cognitive workload monitoring. To evaluate the effectiveness of the proposed system, 15 participants were recruited to perform the surgical task with and without the proposed system. The participants' task performance and perceived workload were collected and compared.

RESULTS

The proposed neural network model achieved an accuracy of 77.9% in cognitive workload classification. In addition, better task performance and lower perceived workload were observed when participants completed the experimental task under the task condition supplemented with adaptive aiding using the proposed system.

CONCLUSIONS

The proposed MWL monitoring system successfully diminished the perceived workload of participants and increased their task performance under high-stress conditions via interventions by a semi-autonomous suction tool. The preliminary results from the comparative study show the potential impact of automated adaptive aiding systems in enhancing surgical task performance via cognitive workload-triggered interventions in robotic-assisted surgery.

Understanding mental fatigue and its detection: a comparative analysis of assessments and tools

Mental fatigue has shown to be one of the root causes of decreased productivity and overall cognitive performance, by decreasing an individual's ability to inhibit responses, process information and concentrate. The effects of mental fatigue have led to occupational errors and motorway accidents. Early detection of mental fatigue can prevent the escalation of symptoms that may lead to chronic fatigue syndrome and other disorders. To date, in clinical settings, the assessment of mental fatigue and stress is done through self-reported questionnaires. The validity of these questionnaires is questionable, as they are highly subjective measurement tools and are not immune to response biases. This review examines the wider presence of mental fatigue in the general population and critically compares its various detection techniques (i.e., self-reporting questionnaires, heart rate variability, salivary cortisol levels, electroencephalogram, and saccadic eye movements). The ability of these detection tools to assess inhibition responses (which are sensitive enough to be manifested in a fatigue state) is specifically evaluated for a reliable marker in identifying mentally fatigued individuals. In laboratory settings, antisaccade tasks have been long used to assess inhibitory control and this technique can potentially serve as the most promising assessment tool to objectively detect mental fatigue. However, more studies need to be conducted in the future to validate and correlate this assessment with other existing measures of mental fatigue detection. This review is intended for, but not limited to, mental health professionals, digital health scientists, vision researchers, and behavioral scientists.

Evaluating Medic Performance in Combat Casualty Care Simulation and Training: A Scoping Review of Prospective Research

INTRODUCTION

Combat medics are required to perform highly technical medical procedures in austere environments with minimal error. Effective means to quantify medic performance in field and simulated environments are critical to optimize medic training procedures as well as to evaluate the influence of medical equipment and other supportive technologies on medic performance. Human performance evaluation in combat casualty care presents many unique challenges due to the unique environment (battlefields) and population (medics) that must be represented. Recent advances in simulation and measurement technology have presented opportunities to improve simulation fidelity and measurement quality; however, it is currently unclear to what extent these advances have been adopted in this domain.

METHODOLOGY

In this work, a scoping review of recent (2011-2021) prospective research on Army medic (68 W and Special Operations) performance is presented. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines served as the framework for the review. The goal of this work was to summarize recent trends and practices and to illuminate opportunities for future work. Two human factors experts performed an exhaustive review of relevant, peer-reviewed literature and systematically identified articles for inclusion in the final analysis. The articles were examined in detail, and data elements of interest were extracted.

RESULTS

Forty-eight articles were identified based on the defined inclusion criteria. Thirty three of the articles focused on technological evaluation, 25 focused on medic training procedures, and 5 focused on evaluating medical techniques. Study contributions were predominantly related to medic training materials/procedures and simulator technology. Supportive medical technologies, including telemedical systems, hemorrhage control devices, and ultrasound devices, also received significant attention. Timing was the most common metric used to quantify medic performance, followed by skill pass/fail ratings. There was a notable lack of neurophysiological data used to examine medic physical/cognitive workload during procedures, a growing practice in many other related domains. The most commonly simulated procedures were hemorrhage control, airway management, and thoracostomy. Notable limitations cited across articles were insufficient simulation fidelity, inadequate sample size or sample representativeness, and poor study design.

CONCLUSIONS

This work provided a summary of recent peer-reviewed research related to medic simulation and training, and performance evaluation. This article should be used to contextualize existing research and inspire new research questions. Expanding and advancing research on medic simulation and training will help to ensure optimal casualty care at the front lines.

Autonomic Regulation of Facial Temperature during Stress: A Cross-Mapping Analysis

Skin temperature reflects the Autonomic Nervous System (ANS)'s response to emotions and mental states and can be remotely measured using InfraRed Thermography. Understanding the physiological mechanisms that affect facial temperature is essential to improve the precision of emotional inference from thermal imaging. To achieve this aim, we recorded thermal images from 30 volunteers, at rest and under acute stress induced by the Stroop test, together with two autonomic correlates, i.e., heart rate variability and electrodermal activity, the former serving as a measure of cardiovascular dynamics, and the latter of the activity of the sweat glands. We used a Cross Mapping (CM) approach to quantify the nonlinear coupling of the temperature from four facial regions with the ANS correlates. CM reveals that facial temperature has a statistically significant correlation with the two autonomic time series, under both conditions, which was not evident in the linear domain. In particular, compared to the other regions, the nose shows a significantly higher link to the electrodermal activity in both conditions, and to the heart rate variability under stress. Moreover, the cardiovascular activity seems to be primarily responsible for the well-known decrease in nose temperature, and its coupling with the thermal signals significantly varies with gender.

Interactive teaching enhances students' physiological arousal during online learning

The pure transfer of face-to-face teaching to a digital learning environment can be accompanied by a significant reduction in the physiological arousal of students, which in turn can be associated with passivity during the learning process, often linked to insufficient levels of concentration and engagement in the course work. Therefore, the aim of this study was to investigate whether students' psychobiological stress responses can be enhanced in the context of anatomical online learning and how increased physiological parameters correlate with characteristics of learning experiences in a digital learning environment. Healthy first-year medical students (n = 104) experienced a regular practical course in Microscopic Anatomy either in face-to-face learning, in passive online learning or in an interaction-enhanced version of online learning. Compared to passive online learning, students engaged in the interaction-enhanced version of online learning displayed a significantly reduced Heart Rate Variability (P 0.001, partial η² = 0.381) along with a strong increase in salivary cortisol (P 0.001, partial η² = 0.179) and salivary alpha-amylase activity (P 0.001, partial η² = 0.195). These results demonstrated that the physiological arousal of students engaged in online learning can be enhanced via interactive teaching methods and pointed towards clear correlations between higher physiological responses and elementary criteria of learning experience such as engagement and attention.

Channeling of Brain Towards Engaging Sensorimotor Tasks for Inducing Hedonic Pleasure to Alleviate Blood Pressure in Hypertension

Background

Hypertension is a modifiable risk factor for cardiovascular disease and is responsible for major deaths due to stroke and coronary heart disease. Several pharmacological and non-pharmacological interventions for reducing blood pressure have been tried earlier. Modulating brain regions such as prefrontal cortex (PFC) to channelize activities is an effective tool to target blood pressure.

Purpose

Prefrontal cortex (PFC) exerts inhibitory control over sympathoexcitatory circuits, which was explored using a novel reaction time paradigm.

Methods

Thirty participants of both genders in the age group 40-70 years with established hypertension were included. A structured reaction time paradigm was designed to include psychomotor and visuomotor elements with integrated sensory attention and motor performance tasks. Blood pressure, Lead II ECG, and EEG from F3 and F4 were recorded. A paired t-test was used to examine the variations in these parameters across tasks.

Results

A significant reduction in mean arterial pressure by 4.04 mmHg (p = .0232) during the visuomotor task and a reduction of 3.38 mmHg during the auditory cue task (p = .0446) were observed. Analysis of the difference in heart rate has shown a profound decrease after passive listening tasks by 3.7 beats (p < .0001*). Spectral analysis from F3 and F4 shows high power in low-frequency zone of EEG indicating a relaxed state during auditory cues and passive listening.

Conclusion

The reaction time paradigm, when applied to hypertensives, helped decrease blood pressure and heart rate and improved the high frequency (HF) component of heart rate variability, indicating parasympathetic dominance. Such reward-oriented paradigms may act as biofeedback modules that cause hyperactivity of the PFC to suppress the sympathoexcitatory circuit with increased parasympathetic activity beneficial to hypertensive individuals.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.

Dynamic fluctuations in ascending heart-to-brain communication under mental stress

Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between electroencephalogram (EEG) oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the γ band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand.

The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations

BACKGROUND

No platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods.

METHODS

EKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals.

RESULTS

Relative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e-04; P = 3.25e-05), 3.08% (S.E. 1.603e-03; P < 2e-16) and 1.19% (S.E. 2.631e-03; P = 5.66e-06), respectively, during error. Relative LF RMS power decreased 1.44% (S.E. 2.337e-03; P = 8.38e-10), and relative HF RMS power increased 5.51% (S.E. 1.945e-03; P < 2e-16).

CONCLUSIONS

Use of a novel, on-line biometric and operating room data capture and analysis platform enabled detection of distinct operator physiological changes during intraoperative errors. Monitoring operator EKG metrics during surgery may help improve patient outcomes through real-time assessments of intraoperative surgical proficiency and perceived difficulty as well as inform personalized surgical skills development.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Physiological and perceptual consequences of trust in collaborative robots: An empirical investigation of human and robot factors

Measuring trust is an important element of effective human-robot collaborations (HRCs). It has largely relied on subjective responses and thus cannot be readily used for adapting robots in shared operations, particularly in shared-space manufacturing applications. Additionally, whether trust in such HRCs differ under altered operator cognitive states or with sex remains unknown. This study examined the impacts of operator cognitive fatigue, robot reliability, and operator sex on trust symptoms in collaborative robots through both objective measures (i.e., performance, heart rate variability) and subjective measures (i.e., surveys). Male and female participants were recruited to perform a metal surface polishing task in partnership with a collaborative robot (UR10), in which they underwent reliability conditions (reliable, unreliable) and cognitive fatigue conditions (fatigued, not fatigued). As compared to the reliable conditions, unreliable robot manipulations resulted in perceived trust, an increase in both sympathetic and parasympathetic activity, and operator-induced reduction in task efficiency and accuracy but not precision. Cognitive fatigue was shown to correlate with higher fatigue scores and reduced task efficiency, more severely impacting females. The results highlight key interplays between operator states of fatigue, sex, and robot reliability on both subjective and objective responses of trust. These findings provide a strong foundation for future investigations on better understanding the relationship between human factors and trust in HRC as well as aid in developing more diagnostic and deployable measures of trust.

Generalisable machine learning models trained on heart rate variability data to predict mental fatigue

A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to detect fatigue in order to prevent its consequences. However, the results of these studies cannot be generalised because of various methodological issues including the use of only one type of cognitive task to induce fatigue which makes any predictions task-specific. In this study, we combined the datasets of three experiments each of which applied different cognitive tasks for fatigue induction and trained algorithms that detect fatigue and predict its severity. We also tested different time window lengths and compared algorithms trained on resting and task related data. We found that classification performance was best when the support vector classifier was trained on task related HRV calculated for a 5-min time window (AUC = 0.843, accuracy = 0.761). For the prediction of fatigue severity, CatBoost regression showed the best performance when trained on 3-min HRV data and self-reported measures (R² = 0.248, RMSE = 17.058). These results indicate that both the detection and prediction of fatigue based on HRV are effective when machine learning models are trained on heterogeneous, multi-task datasets.

Slow yoga breathing improves mental load in working memory performance and cardiac activity among yoga practitioners

This study investigated the immediate effect of slow yoga breathing (SYB) at 6 breaths per minute (bpm) simultaneously on working memory performance and heart rate variability (HRV) in yoga practitioners. A total of 40 healthy male volunteers performed a working memory task, ‘n-back’, consisting of three levels of difficulty, 0-back, 1-back, and 2-back, separately, before and after three SYB sessions on different days. The SYB sessions included alternate nostril breathing (ANB), right nostril breathing (RNB), and breath awareness (BAW). Repeated measures analysis of variance showed a significant reduction in reaction time (ms) in 2-back condition immediately after ANB (−8%), RNB (−8%) and BAW (−5%) practices. Similarly, the accuracy was improved in the 0-back condition after RNB (4%), and in the 2-back condition after ANB (6%) and RNB (6%) practices. These results suggest that SYB practice enhances cognitive abilities (8–9%) related to memory load and improves the functioning of cardiac autonomic activity, which is required for the successful completion of mental tasks.Trial registered in the Clinical Trials Registry of India (CTRI/2018/01/011132).

Investigating Methods for Cognitive Workload Estimation for Assistive Robots

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

Effects of overnight military training and acute battle stress on the cognitive performance of soldiers in simulated urban combat

Understanding the effect of stress, fatigue, and sleep deprivation on the ability to maintain an alert and attentive state in an ecologically valid setting is of importance as lapsing attention can, in many safety-critical professions, have devastating consequences. Here we studied the effect of close-quarters battle (CQ battle) exercise combined with overnight military training with sleep deprivation on cognitive performance, namely sustained attention and response inhibition. In addition, the effect of the CQ battle and overnight training on cardiac activity [heart rate and root mean square of the successive differences (RMSSD)] during the cognitive testing and the relationship between cardiac activity and cognitive performance were examined. Cognitive performance was measured with the psychomotor vigilance task (PVT) and the sustained attention to response task (SART). Altogether 45 conscripts participated in the study. The conscripts were divided into control (CON) and experimental (EXP) groups. The CON completed the training day after a night of sleep and the EXP after the overnight military training with no sleep. Results showed that the effect of the overnight training on cognitive performance and the between-group difference in heart rate (HR) and heart rate variability (HRV) depended on the cognitive test. Surprisingly, the cognitive performance was not largely affected by the CQ battle. However, as expected, the CQ battle resulted in a significant decrease in RMSSD and an increase in HR measured during the cognitive testing. Similarly, the HR parameters were related to cognitive performance, but the relationship was found only with the PVT. In conclusion, fatigue due to the overnight training impaired the ability to maintain sufficient alertness level. However, this impairment in arousal upregulation was counteracted by the arousing nature of the SART. Hence, the conscripts' cognitive performance was mainly preserved when performing a stimulating task, despite the fatigue from the sleep loss of the preceding night and physical activity.

Are there neurophysiological differences behind the play of different chess modalities?: An international grandmaster case study

The study of mental load is an emerging research topic in the field of sport sciences. In the sport of chess, there is a need to understand the mental demands of the sport of chess in order to manage training loads. The present study aimed to analyze the electrical brain pattern of an elite chess player during different chess games: 15 + 10, blindfold 15 + 10, lightning game, and problem-solving chess tasks. The participant was a male 33-year-old chess player with 2562 points of ELO and more than 26 years of chess experience, training between 3 and 4 h a day. The 15 + 10 game consisted of 15 min + 10 s increment per move. In the blindfold game, the participant cannot see the positions of the pieces and does not touch them. In the lightning game, the participant played four consecutive one minute game. In addition, two high-level, two low-level, and two medium-level chess problems were performed. Electroencephalographic (EEG) and heart rate variability (HRV) responses were measured to assess the electrical brain pattern and autonomic modulation respectively. The participant won both games at 15 + 10 (normal and blindfold), lost three and drew one of the four lightning games, solved the two low-level and the two medium-level problems and solved one of the two high-level problems. Although in both games the player achieved victory the average player move quality measured with both pure analysis and analysis with additional limitations were lower in 15 + 10 blindfold game than in 15 + 10 game. Increments in theta and alpha power spectrums can be observed during the most demanding chess games (blindfold chess, lightning game, and the chess problem at difficult- level). Furthermore, the highest alpha power spectrum values can be observed during blindfold chess. In conclusion, this is the first study investigating the EEG pattern of a International Chess Grandmaster during a blindfold chess game. We found that in the chess games where lower performance is exhibited, theta and alpha power spectrums increased.

Test-retest reliability of heart-rate variability metrics in individuals with aphasia

Individuals with aphasia may have impairments in cognition, higher incidences of depression, and a variety of post-stroke functional impairments. However, evaluating these issues is challenging since most assessments require some degree of linguistic processing and task instructions are often verbal. Heart rate variability has shown to be an objective marker for cognitive functioning in neurologically healthy individuals, depression and post-stroke depression, and post-stroke functional impairments. However, before the utility of heart rate variability is established in persons with aphasia, its test-retest reliability needs to be established. The purpose of this study was to assess test-retest reliability of heart rate variability metrics in persons with aphasia. Heart rate variability was recorded at rest while in a sitting position in twenty-one persons with aphasia at two time points. Heart rate variability metrics were mostly moderately reliable. The majority of participants had resting heart rate variability parasympathetic and sympathetic indexes similar to those of neurologically healthy individuals. Further research is needed to establish the test-retest reliability of heart rate variability metrics in different postures and breathing conditions in persons with aphasia.

The Impact of Control Interface on Features of Heart Rate Variability

Shared human-robot control for assistive machines can improve the independence of individuals with motor impairments. Monitoring elevated levels of workload can enable the assistive autonomy to adjust the control sharing in an assist-as-needed way, to achieve a balance between user fatigue, stress, and independent control. In this work, we aim to investigate how heart rate variability features can be utilized to monitor elevated levels of mental workload while operating a powered wheelchair, and how that utilization might vary under different control interfaces. To that end, we conduct a 22 person study with three commercial interfaces. Our results show that the validity and reliability of using the ultra-short-term heart-rate variability features as predictors of workload indeed are affected by the type of interface in use.

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

The Continuous Matching Task (CMT) - real-time procedural stimulus generation for adaptive testing of attention

The Continuous Matching Task (CMT) is a novel paradigm designed to measure sustained attention and alertness. It is a special type of Continuous Performance Task (CPT) that utilizes truly continuous stimulus material. Stimuli are generated in real-time by a procedural algorithm which also enables adaptive testing. The task is highly flexible and can be used in either single or dual-task configurations that also allow for task mixing. The functionality of the algorithm and applications are presented. The viability of the CMT is tested and results are compared with similar tasks, i.e. Stroop-Task and Conner's CPT (CCPT), as well as self-reports of ADHD in adults in a Multi-Trait-Multi-Method approach in a sample of N = 122 participants. Self-reports and measurements of heart rate variability during testing are analyzed to infer and compare mental workload during tasks. Overall, variants of the CMT induce a higher mental workload than the other tasks, and employing the dual-task CMT with adaptive difficulty resulted in the highest reliability and validity. Results indicate that the CMT is primarily a measure of alertness and processing speed and benefits from adaptive testing.

Use of wearable physiological sensors to predict cognitive workload in a visuospatial learning task

BACKGROUND

Increased cognitive workload, sometimes known as mental strain or mental effort, has been associated with reduced performance.

OBJECTIVE

The use of physiological monitoring was investigated to predict cognitive workload and performance.

METHODS

Twenty-one participants completed a 10-minute seated rest, a visuospatial learning task modeled after crane operation, and the Stroop test, an assessment that measures cognitive interference. Heart rate, heart rate variability, electrodermal activity, skin temperature, and electromyographic activity were collected.

RESULTS

It was found that participants' ability to learn the simulated crane operation task was inversely correlated with self-reported frustration. Significant changes were also found in physiological metrics in the simulation with respect to rest, including an increase in heart rate, electrodermal activity, and trapezius muscle activity; heart rate and muscle activity were also correlated with simulation performance. The relationship between physiological measures and self-reported workload was modeled and it was found that muscle activity and high frequency power, a measure of heart rate variability, were significantly associated with the workload reported.

CONCLUSIONS

The findings support the use of physiological monitoring to inform real time decision making (e.g., identifying individuals at risk of injury) or training decisions (e.g., by identifying individuals that may benefit from additional training even when no errors are observed).

Usability and Effectiveness of Immersive Virtual Grocery Shopping for Assessing Cognitive Fatigue in Healthy Controls: Protocol for a Randomized Controlled Trial

BACKGROUND

Cognitive fatigue (CF) is a human response to stimulation and stress and is a common comorbidity in many medical conditions that can result in serious consequences; however, studying CF under controlled conditions is difficult. Immersive virtual reality provides an experimental environment that enables the precise measurement of the response of an individual to complex stimuli in a controlled environment.

OBJECTIVE

We aim to examine the development of an immersive virtual shopping experience to measure subjective and objective indicators of CF induced by instrumental activities of daily living.

METHODS

We will recruit 84 healthy participants (aged 18-75 years) for a 2-phase study. Phase 1 is a user experience study for testing the software functionality, user interface, and realism of the virtual shopping environment. Phase 2 uses a 3-arm randomized controlled trial to determine the effect that the immersive environment has on fatigue. Participants will be randomized into 1 of 3 conditions exploring fatigue response during a typical human activity (grocery shopping). The level of cognitive and emotional challenges will change during each activity. The primary outcome of phase 1 is the experience of user interface difficulties. The primary outcome of phase 2 is self-reported CF. The core secondary phase 2 outcomes include subjective cognitive load, change in task performance behavior, and eye tracking. Phase 2 uses within-subject repeated measures analysis of variance to compare pre- and postfatigue measures under 3 conditions (control, cognitive challenge, and emotional challenge).

RESULTS

This study was approved by the scientific review committee of the National Institute of Nursing Research and was identified as an exempt study by the institutional review board of the National Institutes of Health. Data collection will begin in spring 2021.

CONCLUSIONS

Immersive virtual reality may be a useful research platform for simulating the induction of CF associated with the cognitive and emotional challenges of instrumental activities of daily living.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04883359; http://clinicaltrials.gov/ct2/show/NCT04883359.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/28073.

Relationship between the level of mental fatigue induced by a prolonged cognitive task and the degree of balance disturbance

This study investigated the effects of mental fatigue (MF) induced by a 90-min AX-continuous performance test (AX-CPT) on balance control by addressing the issue of the heterogeneity of individuals' responses. Twenty healthy young active participants were recruited. They had to carry out two balance tasks (sway as little as possible on a stable support with the eyes open and closed) when standing on a force platform before and after performing a 90-min AX-CPT. The NASA-TLX test was used to assess the subjective manifestations of MF. Objective cognitive performance was measured using results from the AX-CPT. Inter-individual differences in behavioral deterioration due to MF were analyzed with a hierarchical cluster analysis, which categorizes participants' behaviors into subgroups with similar characteristics. The cluster analysis revealed that the achievement of the AX-CPT induced various levels of MF and balance impairments within the whole sample. A significant relationship between the level of MF and the degree of balance disturbance was observed only when participants stood with the eyes open, thus suggesting that inter-individual differences in vulnerability to MF could stem from differences between subjects in the level of engagement of visual attention and/or from differences in field dependency for balance control. These findings show that the completion of the same prolonged demanding cognitive task induces a strong heterogeneity in subjects' responses, with marked individual differences in MF vulnerability that affect balance control differently according to the sensory context.

Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial

INTRODUCTION

The diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI.

METHODS AND ANALYSIS

This study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.

Enhanced cardiac vagal tone in mental fatigue: Analysis of heart rate variability in Time-on-Task, recovery, and reactivity

Heart Rate Variability (HRV) has been suggested as a useful tool to assess fatigue-sensitive psychological operations. The present study uses a between and within-subject design with a cognitively demanding task and a documentary viewing condition, to examine the temporal profile of HRV during reactivity, Time-on-Task (ToT), and recovery. In the cognitive task group, participants worked on a bimodal 2-back task with a game-like character (the Gatekeeper task) for about 1.5 hours, followed by a 12-minute break, and a post-break block of performance (about 18 min). In the other group, participants watched documentaries. We hypothesized an increasing vagal-mediated HRV as a function of Time spent on the Gatekeeper task and no HRV change in the documentary viewing group. We also analyzed the trial-based post-response cardiac activity as a physiological associate of task-related motivation. Relative to the documentary-viewing, ToT was associated with an elevated level of subjective fatigue, decreased heart rate, and increased HRV, particularly in the vagal-mediated components. Based on fatigued participants' post-error cardiac slowing, and post-error reaction time analyses, we found no evidence for motivation deficits. The present findings suggest that the parasympathetic branch of the autonomous nervous system functioning as a relaxation system tends to be activated under increasing mental fatigue. In addition, the study shows that many HRV indices also seem to change when individuals are engaged in a prolonged, less fatiguing activity (e.g. documentary viewing). This finding emphasizes the relevance of comparisons/control conditions in ToT experiments.

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient’s state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant’s resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02).

Validity of the Polar V800 Monitor for Assessing Heart Rate Variability in Elderly Adults under Mental Stress and Dual Task Conditions

Background: Aging may result in autonomic nervous dysfunction. Heart rate variability (HRV) is a non-invasive method to measure autonomic nervous activities. Many studies have shown that HRV contributes to the risk assessment of diseases. A Polar V800 heart rate monitor is a wearable device that measures R-R intervals, but has only been validated in younger adults under limited testing conditions. There is no validation of the V800 under mental stress or in dual task testing conditions. Therefore, this study investigated the validity of the Polar V800 heart rate monitor for assessing R-R intervals and evaluated if there were differences on HRV parameters under different situations in community-dwelling elderly adults. Methods: Forty community-dwelling elderly adults were recruited. Heart rates were recorded via electrocardiogram (ECG) and the V800 under sitting, during an arithmetic test, during a naming test, a self-selected walking velocity test (SSWV), and dual tasks (SSWV performing mental arithmetic test and SSWV performing naming test). Indices of time and frequency domains of HRV were calculated afterwards. The intra-class correlation coefficient (ICC) analysis and effect size were calculated to examine the concurrent validity between the V800 and the ECG. Results: All HRV indices from the V800 were highly correlated with the ECG under all tested conditions (ICC = 0.995–1.000, p < 0.001) and the effect size of bias was small (<0.1). Conclusion: Overall, the V800 has good validity on the assessment of HRV in community-dwelling elderly adults during sitting, mental arithmetic test, naming test, SSWV, and dual tasks.

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

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

Complexus cordis

The science-based study of the heart has allowed us to know its structure and function deeply, through the fragmentation and analysis of its parts, following the guidelines that so many achievements have given to us. However, at the time of reassembling those analyzed fragments, we realize that something is missing; the simply sum of the parts is not equal to everything. Thus, for decades, numerous scientists have studied novel strategies that allow us understanding, every natural phenomena from a more inclusive, open and integrative models, which closely address to interactions rather than components. In this way, we can observe how, the behavior of many variables usually transgress the conventional plane and moves towards non-linearity and fractality, making a complex tissue that will maintain its structure while thermodynamically viable. Thus, this document shows the way how, the non-linear study of complex cardiovascular dynamics, begins to give us answers to many questions that the clinical cardiologist poses every day.

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.

Non-linear analysis of heart rate variability for evaluating the acute effects of caffeinated beverages in young adults

Caffeinated beverages are the most consumed substances in the world. High rate of uptake of these beverages leads to various cardiovascular disorders ranging from palpitations to coronary failure. The objective of the study is to ascertain how the complexity parameters of heart rate variability are affected by acute consumption of caffeinated beverages in young adults.Electrocardiogram measurements were performed before consuming drinks. After consuming the drinks, measurements were done again at 30 minutes and 60 minutes. Heart rate variability signals were acquired from electrocardiogram signals. Also, the signals were reconstructed in the phase space and largest Lyapunov exponent, correlation dimension, approximate entropy, and detrended fluctuation analysis values were calculated.Heart rate increased for energy drink and cola groups but not in coffee group. Non-linear parameter values of energy drink, coffee, and cola group are increased within 60 minutes after drink consumption. This change is statistically significant just for energy drink group.Energy drink consumption increases the complexity of the cardiovascular system in young adults significantly. Coffee and cola consumption have no significant effect on the non-linear parameters of heart rate variability.

Heart Rate and Heart Rate Variability Correlate with Clinical Reasoning Performance and Self-Reported Measures of Cognitive Load

Cognitive load is a key mediator of cognitive processing that may impact clinical reasoning performance. The purpose of this study was to gather biologic validity evidence for correlates of different types of self-reported cognitive load, and to explore the association of self-reported cognitive load and physiologic measures with clinical reasoning performance. We hypothesized that increased cognitive load would manifest evidence of elevated sympathetic tone and would be associated with lower clinical reasoning performance scores. Fifteen medical students wore Holter monitors and watched three videos depicting medical encounters before completing a post-encounter form and standard measures of cognitive load. Correlation analysis was used to investigate the relationship between cardiac measures (mean heart rate, heart rate variability and QT interval variability) and self-reported measures of cognitive load, and their association with clinical reasoning performance scores. Despite the low number of participants, strong positive correlations were found between measures of intrinsic cognitive load and heart rate variability. Performance was negatively correlated with mean heart rate, as well as single-item cognitive load measures. Our data signify a possible role for using physiologic monitoring for identifying individuals experiencing high cognitive load and those at risk for performing poorly during clinical reasoning tasks.

Autonomic Nervous System Responses to Whole-Body Vibration and Mental Workload: A Pilot Study

Background:

Whole-body vibration (WBV) and mental workload (MWL) are common stressors among drivers who attempt to control numerous variables while driving a car, bus, or train.

Objective:

To examine the individual and combined effects of the WBV and MWL on the autonomic nervous system.

Methods:

ECG of 24 healthy male students was recorded using NeXus-4 while performing two difficulty levels of a computerized dual task and when they were exposing to WBV (intensity 0.5 m/s2; frequency 3–20 Hz). Each condition was examined for 5 min individually and combined. Inter-beat intervals were extracted from ECG records. The time-domain and frequency-domain heart rate variability parameters were then extracted from the inter-beat intervals data.

Results:

A significant (p=0.008) increase was observed in the mean RR interval while the participants were exposed to WBV; there was a significant (p=0.02) reduction in the mean RR interval while the participants were performing the MWL. WBV (p=0.02) and MWL significantly (p<0.001) increased the standard deviation of normal-to-normal intervals with a moderate-to-large effect size. All active periods increased the low-frequency component and low-frequency/high-frequency ratio. However, only the WBV significantly increased the highfrequency component. A significant (p=0.01) interaction was observed between the WBV and MWL on low-frequency component and low-frequency/high-frequency ratio.

Conclusion:

Exposure to WBV and MWL can dysregulate the autonomic nervous system. WBV stimulates both sympathetic and parasympathetic nervous system; MWL largely affects sympathetic nervous system. Both variables imbalance the sympatho-vagal control as well.

Falk T. Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24.41% in accuracy and of 27.97% in F1 score can be achieved even at high activity levels.