Conscious monitoring and control (reinvestment) in surgical performance under pressure.

Research on intraoperative stressors has focused on external factors without considering individual differences in the ability to cope with stress. One individual difference that is implicated in adverse effects of stress on performance is "reinvestment," the propensity for conscious monitoring and control of movements. The aim of this study was to examine the impact of reinvestment on laparoscopic performance under time pressure

Pilot workload and fatigue: A critical survey of concepts and assessment techniques

The principal unresolved issues in conceptualizing and measuring pilot workload and fatigue are discussed. These issues are seen as limiting the development of more useful working concepts and techniques and their application to systems engineering and management activities. A conceptual analysis of pilot workload and fatigue, an overview and critique of approaches to the assessment of these phenomena, and a discussion of current trends in the management of unwanted workload and fatigue effects are presented. Refinements and innovations in assessment methods are recommended for enhancing the practical significance of workload and fatigue studies

Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play

Feeling Math Effects of Stereotype Threat, Evaluator Apprehension, and Social Identity on Heart Rate Variability and Female Math Performance

This study investigated the relationships between stereotype threat, evaluator apprehension, and social math identity on female math performance and heart rate variability. Participants’ received either a sex or community prime and completed a written math assessment and verbal math task in the presence of an evaluator as heart rate variability was recorded. Evaluator sex was manipulated, and participants were surveyed and scored as either “weak” or “strong” in social math identity. Analysis by ANOVA failed to reproduce findings detailing depressed mathematical performance with regards to sex prime or heart rate variability. Regression found both social math identity and evaluator sex to significantly predict heart rate variability (increased) at a time of stress during the experiment

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 203

This bibliography lists 150 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1980

Heart Rate Variability (HRV) biofeedback: a new training approach for operator’s performance enhancement

The widespread implementation of advanced and complex systems requires predominantly operators’ cognitive functions and less importance of human manual control. On the other hand, most operators perform their cognitive functions below their peak cognitive capacity level due to fatigue, stress, and boredom. Thus, there is a need to improve their cognitive functions during work. The goal of this paper is to present a psychophysiology training approach derived from cardiovascular response named heart rate variability (HRV) biofeedback. Description of resonant frequency biofeedback - a specific HRV training protocol - is discussed as well as its supported researches for the performance enhancement. HRV biofeedback training works by teaching people to recognize their involuntary HRV and to control patterns of this physiological response. The training is directed to increase HRV amplitude that promotes autonomic nervous system balance. This balance is associated with improved physiological functioning as well as psychological benefits. Most individuals can learn HRV biofeedback training easily which involves slowing the breathing rate (around six breaths/min) to each individual’s resonant frequency at which the amplitude of HRV is maximized. Maximal control over HRV can be obtained in most people after approximately four sessions of training. Recent studies have demonstrated the effectiveness of HRV biofeedback to the improvement of some cognitive functions in both simulated and real industrial operators.Peer Reviewe

iMind: Uma ferramenta inteligente para suporte de compreensão de conteúdo

Usually while reading, content comprehension difficulty affects individual performance. Comprehension difficulties, e. g., could lead to a slow learning process, lower work quality, and inefficient decision-making. This thesis introduces an intelligent tool called “iMind” which uses wearable devices (e.g., smartwatches) to evaluate user comprehension difficulties and engagement levels while reading digital content. Comprehension difficulty can occur when there are not enough mental resources available for mental processing. The mental resource for mental processing is the cognitive load (CL). Fluctuations of CL lead to physiological manifestation of the autonomic nervous system (ANS), which can be measured by wearables, like smartwatches. ANS manifestations are, e. g., an increase in heart rate. With low-cost eye trackers, it is possible to correlate content regions to the measurements of ANS manifestation. In this sense, iMind uses a smartwatch and an eye tracker to identify comprehension difficulty at content regions level (where the user is looking). The tool uses machine learning techniques to classify content regions as difficult or non-difficult based on biometric and non-biometric features. The tool classified regions with a 75% accuracy and 80% f-score with Linear regression (LR). With the classified regions, it will be possible, in the future, to create contextual support for the reader in real-time by, e.g., translating the sentences that induced comprehension difficulty.Normalmente durante a leitura, a dificuldade de compreensão pode afetar o desempenho da leitura. A dificuldade de compreensão pode levar a um processo de aprendizagem mais lento, menor qualidade de trabalho ou uma ineficiente tomada de decisão. Esta tese apresenta uma ferramenta inteligente chamada “iMind” que usa dispositivos vestíveis (por exemplo, smartwatches) para avaliar a dificuldade de compreensão do utilizador durante a leitura de conteúdo digital. A dificuldade de compreensão pode ocorrer quando não há recursos mentais disponíveis suficientes para o processamento mental. O recurso usado para o processamento mental é a carga cognitiva (CL). As flutuações de CL levam a manifestações fisiológicas do sistema nervoso autônomo (ANS), manifestações essas, que pode ser medido por dispositivos vestíveis, como smartwatches. As manifestações do ANS são, por exemplo, um aumento da frequência cardíaca. Com eye trackers de baixo custo, é possível correlacionar manifestação do ANS com regiões do texto, por exemplo. Neste sentido, a ferramenta iMind utiliza um smartwatch e um eye tracker para identificar dificuldades de compreensão em regiões de conteúdo (para onde o utilizador está a olhar). Adicionalmente a ferramenta usa técnicas de machine learning para classificar regiões de conteúdo como difíceis ou não difíceis com base em features biométricos e não biométricos. A ferramenta classificou regiões com uma precisão de 75% e f-score de 80% usando regressão linear (LR). Com a classificação das regiões em tempo real, será possível, no futuro, criar suporte contextual para o leitor em tempo real onde, por exemplo, as frases que induzem dificuldade de compreensão são traduzidas