Sex differences in autonomic functions and cognitive performance during cold-air exposure and cold-water partial immersion.

Introduction: This study investigated the differences between males and females in autonomic functions and cognitive performance during cold-air exposure and cold-water partial-immersion compared to a room temperature-air environment. Although several studies have investigated the effects of cold-air or cold-water exposures on autonomic function and cognitive performance, biological sex differences are often under-researched.

Methods: Twenty-two males and nineteen females participated in the current study.

Performance During a Task That Simulates Passive Sonar Operator Duties Under Conditions of Varying Workloads.

Introduction: It is critical to develop and implement lab-based computer experiments that simulate real-world tasks in order to characterize operational requirements and challenges or identify potential solutions. Achieving a high degree of laboratory control, operational generalizability, and ease-of-use for a task is challenging, often leading to the development of tasks that can satisfy some facets but not all. This can result in insufficient solutions that leave real-world stakeholders with unsolved problems.

Activation thresholds, not quitting thresholds, account for the low prevalence effect in dynamic search.

The low-prevalence effect (LPE) is the finding that target detection rates decline as targets become less frequent in a visual search task. A major source of this effect is thought to be that fewer targets result in lower quitting thresholds, i.e.

Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking.

High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes.

Facial features and head movements obtained with a webcam correlate with performance deterioration during prolonged wakefulness.

We have performed a direct comparison between facial features obtained from a webcam and vigilance-task performance during prolonged wakefulness. Prolonged wakefulness deteriorates working performance due to changes in cognition, emotion, and by delayed response. Facial features can be potentially collected everywhere using webcams located in the workplace.

Brain Activity Correlates With Cognitive Performance Deterioration During Sleep Deprivation.

We studied the correlation between oscillatory brain activity and performance in healthy subjects performing the error awareness task (EAT) every 2 h, for 24 h. In the EAT, subjects were shown on a screen the names of colors and were asked to press a key if the name of the color and the color it was shown in matched, and the screen was not a duplicate of the one before ("Go" trials). In the event of a duplicate screen ("Repeat No-Go" trial) or a color mismatch ("Stroop No-Go" trial), the subjects were asked to withhold from pressing the key.

Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity.

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS).

Performance Trends During Sleep Deprivation on a Tilt-Based Control Task.

Background: Understanding human behavior under the effects of sleep deprivation allows for the mitigation of risk due to reduced performance. To further this goal, this study investigated the effects of short-term sleep deprivation using a tilt-based control device and examined whether existing user models accurately predict targeting performance.

Methods: A task in which the user tilts a surface to roll a ball into a target was developed to examine motor performance.

Human Performance Deterioration Due to Prolonged Wakefulness Can Be Accurately Detected Using Time-Varying Spectral Analysis of Electrodermal Activity.

Objective: The aim was to determine if indices of the autonomic nervous system (ANS), derived from the electrodermal activity (EDA) and electrocardiogram (ECG), could be used to detect deterioration in human cognitive performance on healthy participants during 24-hour sleep deprivation.

Background: The ANS is highly sensitive to sleep deprivation.

Methods: Twenty-five participants performed a desktop-computer-based version of the psychomotor vigilance task (PVT) every 2 hours.

Sleep Deprivation in Young and Healthy Subjects Is More Sensitively Identified by Higher Frequencies of Electrodermal Activity than by Skin Conductance Level Evaluated in the Time Domain.

We analyzed multiple measures of the autonomic nervous system (ANS) based on electrodermal activity (EDA) and heart rate variability (HRV) for young healthy subjects undergoing 24-h sleep deprivation. In this study, we have utilized the error awareness test (EAT) every 2 h (13 runs total), to evaluate the deterioration of performance. EAT consists of trials where the subject is presented words representing colors.

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor.

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis.

Statistical analysis of heart rate and heart rate variability monitoring through the use of smart phone cameras.

Video recordings of finger tips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal it is possible to extract a continuous heart rate signal. We performed direct comparisons between 5-lead electrocardiogram based heart rate variability measurements and those obtained from an iPhone 4s and Motorola Droid derived pulsatile signal to determine the accuracy of heart rate variability measurements obtained from the smart phones.