Article

Enhancing the effectiveness of human-robot teaming with a closed-loop system

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Abstract

With technological developments in robotics and their increasing deployment, human-robot teams are set to be a mainstay in the future. To develop robots that possess teaming capabilities, such as being able to communicate implicitly, the present study implemented a closed-loop system. This system enabled the robot to provide adaptive aid without the need for explicit commands from the human teammate, through the use of multiple physiological workload measures. Such measures of workload vary in sensitivity and there is large inter-individual variability in physiological responses to imposed taskload. Workload models enacted via closed-loop system should accommodate such individual variability. The present research investigated the effects of the adaptive robot aid vs. imposed aid on performance and workload. Results showed that adaptive robot aid driven by an individualized workload model for physiological response resulted in greater improvements in performance compared to aid that was simply imposed by the system.

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... -Justification: For the adaptive aid to be useful, the system needs to identify low and high levels of operator workload and respond appropriately. Aid that does not match the workload level is not as helpful (see Teo et al. 2018). ...
... Such distributions indicate that the workload index under Algorithm 1 would be sufficiently able to identify when high workload is reached. In a separate study (Teo et al. 2018), this workload model (i.e., based on Algorithm 1 with the cutoff of 0.62) was implemented in an adaptive aiding system that was driven by workload-related psychophysiological changes. Results of that study indicated that compared with those whose aid was not adaptive, those who received adaptive aid showed greater performance improvements. ...
... This methodology resulted in a viable model that incorporated multiple workload measures and accommodated individual variability in psychophysiological workload responses. The model was used with some success in an adaptive aiding system (Teo et al. 2018). Nevertheless, follow-on work is needed to improve the generalizability of the model to other workload manipulations as well as model sensitivity and specificity. ...
Article
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Potential benefits of technology such as automation are oftentimes negated by improper use and application. Adaptive systems provide a means to calibrate the use of technological aids to the operator’s state, such as workload state, which can change throughout the course of a task. Such systems require a workload model which detects workload and specifies the level at which aid should be rendered. Workload models that use psychophysiological measures have the advantage of detecting workload continuously and relatively unobtrusively, although the inter-individual variability in psychophysiological responses to workload is a major challenge for many models. This study describes an approach to workload modeling with multiple psychophysiological measures that was generalizable across individuals, and yet accommodated inter-individual variability. Under this approach, several novel algorithms were formulated. Each of these underwent a process of evaluation which included comparisons of the algorithm’s performance to an at-chance level, and assessment of algorithm robustness. Further evaluations involved the sensitivity of the shortlisted algorithms at various threshold values for triggering an adaptive aid.
... Hockey et al. 2009). A final limitation of psychophysiology is that workload response is often dominantly dependent on the individual (Teo et al. 2018). Similar to the response specificity principle for arousal (Stephens, Christie, and Friedman 2010), individuals differ quite widely in which metrics prove most sensitive to cognitive demand manipulations. ...
... Existing studies of algorithms that aggregate data from multiple sources to identify workload have utilized a range of machine learning classifiers including artificial neural networks, linear regression, linear discriminant analysis and support vector machines (Heard, Harriott, and Adams 2018). Algorithms can also be personalized to reflect individual variation in the responses most sensitive to workload, in effect assessing workload on a within-rather than a between-subjects basis (Teo et al. 2018). In any case, the focus is on validating the algorithm as means for predicting a significant real-world outcome, rather than identifying any latent workload construct. ...
Article
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We examine the continuing use of subjective workload responses to index an operator’s state, either by themselves or as part of a collective suite of measurements. Lack of convergence of subjective scales with physiological and performance-based measures calls into question whether there is any unitary workload construct that underpins conscious experience, physiological state and the individual’s profile of task-related performance. We examine philosophical and measurement perspectives on the divergence problem, and we consider three possible solutions. First, difficulties in reliable and valid measurement of workload may contribute to divergence but do not fully explain it. Second, workload may be treated operationally: use of specific measures is justified by demonstrating their pragmatic utility in predicting important outcomes. Third, further efforts may be made to develop representational workload measurements that correspond to real empirical phenomena. Application of formal standards for test validity can identify multiple latent constructs supporting subjective workload, including those defining self-regulation in performance contexts. Physiological and performance-based assessments may define additional, distinct constructs. A resolution of the diversity issue is crucial for ergonomics since the invalid application of workload measurement will threaten exposed operators as well as many others who are served by the complex technological systems they control.
... Hockey et al. 2009). A final limitation of psychophysiology is that workload response is often dominantly dependent on the individual ( Teo et al. 2018). Similar to the response specificity principle for arousal (Stephens, Christie, and Friedman 2010), individuals differ quite widely in which metrics prove most sensitive to cognitive demand manipulations. ...
... Existing studies of algorithms that aggregate data from multiple sources to identify workload have utilized a range of machine learning classifiers including artificial neural networks, linear regression, linear discriminant analysis and support vector machines (Heard, Harriott, and Adams 2018). Algorithms can also be personalized to reflect indi- vidual variation in the responses most sensitive to workload, in effect assessing workload on a within-rather than a between-subjects basis ( Teo et al. 2018). In any case, the focus is on validating the algorithm as means for predicting a significant real-world outcome, rather than identifying any latent workload construct. ...
Article
Speeding because of time pressure is a leading contributor to traffic accidents. Previous research indicates that people respond to time pressure through increased physiological activity and by adapting their task strategy in order to mitigate task demands. In the present driving simulator study, we investigated effects of time pressure on measures of eye movement, pupil diameter, cardiovascular and respiratory activity, driving performance, vehicle control, limb movement, head position, and self-reported state. Based on existing theories of human behavior under time pressure, we distinguished three categories of results: (1) driving speed, (2) physiological measures, and (3) driving strategies. Fifty-four participants drove a 6.9-km urban track with overtaking, car following, and intersection scenarios, first with no time pressure (NTP) and subsequently with time pressure (TP) induced by a time constraint and a virtual passenger urging to hurry up. The results showed that under TP in comparison to NTP, participants (1) drove significantly faster, an effect that was also reflected in auxiliary measures such as maximum brake position, throttle activity, and lane keeping precision, (2) exhibited increased physiological activity, such as increased heart rate, increased respiration rate, increased pupil diameter, and reduced blink rate, and (3) adopted scenario-specific strategies for effective task completion, such as driving to the left of the lane during car following, and early visual lookout when approaching intersections. The effects of TP relative to NTP were generally large and statistically significant. However, individual differences in absolute values were large. Hence, we recommend that real-time driver feedback technologies use relative instead of absolute criteria for assessing the driver’s state.
... In such scenarios, the design paradigms known as human-centered design (HCD) (Deutschen Instituts für Normung, 2020) and value-sensitive design (VSD) (Friedman, 1996) have helped to improve the usability of robotic systems for novice operators (Coronado et al., 2021;Eiband et al., 2022), reduce workload by using custom designed interfaces (Pantano et al., 2020), or improve acceptance by changing the appearance of humanoid robots (Kahn et al., 2007). However, to achieve these results, design must aim to establish means of communication that enable humans to build good mental models of the application (Rook, 2013;Sofge, 2013;Hoff and Bashir, 2015;Teo et al., 2018;Demir et al., 2019;Kolbeinsson et al., 2019;Shahrdar et al., 2019). One recent example proving the benefit of good mental models can be seen in the work of (Tausch and Kluge, 2020). ...
Article
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Adoption of human–robot collaboration is hindered by barriers in collaborative task design. A new approach for solving these problems is to empower operators in the design of their tasks. However, how this approach may affect user welfare or performance in industrial scenarios has not yet been studied. Therefore, in this research, the results of an experiment designed to identify the influences of the operator’s self-designed task on physical ergonomics and task performance are presented. At first, a collaborative framework able to accept operator task definition via parts’ locations and monitor the operator’s posture is presented. Second, the framework is used to tailor a collaborative experience favoring decision autonomy using the SHOP4CF architecture. Finally, the framework is used to investigate how this personalization influences collaboration through a user study with untrained personnel on physical ergonomics. The results from this study are twofold. On one hand, a high degree of decision autonomy was felt by the operators when they were allowed to allocate the parts. On the other hand, high decision autonomy was not found to vary task efficiency nor the MSD risk level. Therefore, this study emphasizes that allowing operators to choose the position of the parts may help task acceptance and does not vary operators’ physical ergonomics or task efficiency. Unfortunately, the test was limited to 16 participants and the measured risk level was medium. Therefore, this study also stresses that operators should be allowed to choose their own work parameters, but some guidelines should be followed to further reduce MSD risk levels.
... These risks, related to the design of work tasks, technologies and environments may have a detrimental impact on both the mental and physical health of employees (Brun and Milczarek, 2007). In that regard, themes like trust (Alarcon et al., 2021;Kim et al., 2020), acceptability (Zanchettin et al., 2013), and human-robot teaming (Dehais et al., 2011;Teo et al., 2018), have been preliminarily studied in the field of social and industrial HRI. ...
Article
Industry 4.0 is the concept used to summarize the ongoing fourth industrial revolution, which is profoundly changing the manufacturing systems and business models all over the world. Collaborative robotics is one of the most promising technologies of Industry 4.0. Human-robot interaction and human-robot collaboration will be crucial for enhancing the operator's work conditions and production performance. In this regard, this enabling technology opens new possibilities but also new challenges. There is no doubt that safety is of primary importance when humans and robots interact in industrial settings. Nevertheless, human factors and cognitive ergonomics (i.e. cognitive workload, usability, trust, acceptance, stress, frustration, perceived enjoyment) are crucial, even if they are often underestimated or ignored. Therefore, this work refers to cognitive ergonomics in the design of human-robot collaborative assembly systems. A set of design guidelines has been developed according to the analysis of the scientific literature. Their effectiveness has been evaluated through multiple experiments based on a laboratory case study where different participants interacted with a low-payload collaborative robotic system for the joint assembly of a manufacturing product. The main assumption to be tested is that it is possible to improve the operator's experience and efficiency by manipulating the system features and interaction patterns according to the proposed design guidelines. Results confirmed that participants improved their cognitive response to human-robot interaction as well as the assembly performance with the enhancement of workstation features and interaction conditions by implementing an increasing number of guidelines.
... In recent years, the objective estimation of MWL using neural signals has become an important topic in the field of human factors and neuroergonomics (Tao et al., 2019). Objectively real-time monitoring of MWL using neurophysiological metrics is essential to build closed-loop adaptive aiding systems for complex and safety-critical human-machine systems (Vidulich and Tsang, 2015;Teo et al., 2018Teo et al., , 2020. Although the neuroergonomic methods for MWL estimation have attracted much attention in the past years, the common neuropsychological essence of MWL across different tasks or mental activities is still to be uncovered. ...
Article
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Mental workload (MWL) estimators based on ongoing electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to build adaptive aiding systems for human–machine systems by estimating MWL in real time. However, extracting EEG features which are consistent in indicating MWL across different tasks is still one of the critical challenges. This study attempts to compare the cross-task consistency in indexing MWL variations between two commonly used EEG-based MWL indicators, power spectral density (PSD) of ongoing EEG and task-irrelevant auditory ERPs (tir-aERPs). The verbal N-back and the multi-attribute task battery (MATB), both with two difficulty levels, were employed in the experiment, along with task-irrelevant auditory probes. EEG was recorded from 17 subjects when they were performing the tasks. The tir-aERPs elicited by the auditory probes and the relative PSDs of ongoing EEG between two consecutive auditory probes were extracted and statistically analyzed to reveal the effects of MWL and task type. Discriminant analysis and support vector machine were employed to examine the generalization of tir-aERP and PSD features in indexing MWL variations across different tasks. The results showed that the amplitudes of tir-aERP components, N1, early P3a, late P3a, and the reorienting negativity, significantly decreased with the increasing MWL in both N-back and MATB. Task type had no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERP features. The relative PSDs in θ, α, and low β bands were also sensitive to MWL variations. However, the MWL-sensitive PSD features and their topological patterns were significantly affected by task type. The cross-task classification results based on tir-aERP features also significantly outperformed the PSD features. These results suggest that the tir-aERPs should be potentially more consistent MWL indicators across very different task types when compared to PSD. The current study may provide new insights to our understanding of the common and distinctive neuropsychological essences of MWL across different tasks.
... The level of workload imposed on a human is another important factor affecting human performance in HSI systems. The effects of workload on human performance were investigated in many studies which suggested that both very low and very high levels of workload can cause human performance degradation (McBride, Rogers, & Fisk, 2011;Recchiuto et al., 2016;Teo et al., 2018). Very low levels of workload can result in arousal decrements that cause "out-of-the-loop" problem (M. ...
Thesis
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Human-swarm interaction (HSI) is a research area that studies how human and swarm capabilities can be combined to successfully perform tasks that exceed the performance limits of single robot systems. The main objective of this thesis is to improve the success of HSI by improving the effectiveness of three interdependent elements: the swarm decision-making algorithm in performing its tasks, human interventions in swarm operation, and the interface between the human and the swarm. Swarm decision-making warrants investigation as the state-of-the-art algorithms per-form very poorly under some conditions. Analysing the root causes of such failures reveals that a key performance inhibitor is the unreliable estimation of swarm members’ confidence in their judgements. Two different approaches are proposed to circumvent the identified issues. Performance evaluation under different conditions demonstrates the merits of the proposed approaches and shows that profound improvements to the effective-ness and efficiency of swarm decision-making are possible through the reliable estimation of confidence. Improving swarm effectiveness begets significant benefits to mission performance, but it can negatively affect the effectiveness of human interventions. Previous research has shown that when interacting with a highly reliable machine, humans tend to over-rely on the machine and exhibit notable complacency that limits their ability to detect and fix machine errors. Although over-trust in automation is widely blamed for such complacency, this attribution is yet to be empirically confirmed. This gap is addressed through an empirical investigation of trust in HSI. The results confirm the significant role of trust as a predictor of human reliance on swarm, which suggests that designing trust-aware HSI systems may reduce the negative impacts of human reliance. Utilising a highly reliable swarm while maintaining human vigilance is an objective that might not be possible without an effective human-swarm interface. As automation transparency has proven useful for boosting human understanding of machine operations, it could facilitate human awareness of machine limitations and possible failures. Thus, the thesis empirically examines the efficacy of swarm transparency as a potential intervention for minimising human complacency. The results assert the benefits of transparency in ensuring continued human contributions to the mission even when a highly reliable swarm is used.
... In the second stage, the physio-adaptive system analyses these data to quantify or label the corresponding user state (Fairclough, 2009). Hereby, one can observe a wide range of cognitive (e.g., mental workload - Teo et al., 2018, concentration -Saiwaki et al., 1996 and affective (e.g., frustration - Nasoz et al., 2010, fun -Conn et al., 2008 user states, which can be targeted by the system. Within this stage, two knowledge components are typically proposed for the underlying architecture of a physio-adaptive system: (1) a feature engineering component and (2) an analytics engine. ...
Conference Paper
Physio-adaptive systems define a class of information systems that refer to an innovative mode where system interaction is reached by monitoring, analyzing, and responding to hidden psychophysiological user activity in real-time. However, despite a strong interest of scholars and practitioners in physio-adaptive systems, there exists a lack of a structured and systematic form in which physio-adaptive systems research can be classified. Against this backdrop, this article showcases the current state-of-the-art of physio-adaptive systems research along three different stages, namely (1) collection of physiological data, (2) state determination, as well as (3) system adaptation. Analyzing 44 articles during the years 1994-2019, our main contribution resides in the synopsis of physio-adaptive systems literature along these stages. For instance, we illustrate that there exist three categories for adaptive responses: state display (20% of the analyzed studies), assistance offering (18%), and challenge adaptation (61%). On the grounds of our review, we propose seven promising avenues, which will support scholars in their endeavors on how to pursue with future research in the field of physio-adaptive systems.
... Finally, work in augmented cognition has highlighted the importance of individual differences: metrics are sensitive to both person and task factors (Reinerman-Jones et al., 2014). Because individuals vary in their psychophysiological response to task load, the optimal metric for WL monitoring may vary across operators, and it may be necessary to use "personalized" metrics (Baldwin and Penaranda, 2012;Teo et al., 2018) Using a simulation of unmanned ground vehicle (UGV) operation, we have found that, while task factors such as multi-tasking influence WL response as expected, a substantial part of the variance in Table 3 Criteria used by the DMW to analyze information in the GMC. ...
Article
The Nuclear Regulatory Commission (NRC) has developed a tool to support the understanding and evaluation of workload (WL), situation awareness (SA), and teamwork (TW) metrics used in human factors engineering (HFE) programs for commercial nuclear power plants (NPPs). This article summarizes the NRC report on the tool, and discusses its potential application to other industries. The operational relevance of WL, SA and TW to the NPP domain is reviewed, and challenges associated with the development of valid metrics for these constructs are identified. The Generic Metrics Catalog (GMC) is a database that compiles information about WL, SA, and TW metrics in a readily accessible and easy to use format. It provides information about the psychometric properties of a metric and the soundness of the methods used to investigate these properties. In the evaluation of a proposed HFE in an NPP license application, the Decision Making Wizard (DMW) uses reviewer input and information found in the GMC to identify areas of concern in applications. Tools of this kind may be especially valuable in other domains for managing challenges of novel technologies such as automation, human-robot teaming and augmented cognition.
... Furthermore, psychometric analyses show that metrics from different physiological systems are only weakly correlated at best (Matthews, Reinerman-Jones, Barber, et al., 2015); thus, they index different responses. Individuals may also differ in relation to how discrete physiological systems respond to changing task demands (Teo et al., 2018). Lack of convergence is even more starkly apparent when considering relationships between subjective and physiological response. ...
Article
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Objective: The aim of this study was to distill and define those influences under which change in objective performance level and the linked cognitive workload reflections of subjective experience and physiological variation either associate, dissociate, or are insensitive, one to another. Background: Human factors/ergonomics frequently employs users' self-reports of their own conscious experience, as well as their physiological reactivity, to augment the understanding of changing performance capacity. Under some circumstances, these latter workload responses are the only available assessment information to hand. How such perceptions and physiological responses match, fail to match, or are insensitive to the change in primary-task performance can prove critical to operational success. The reasons underlying these associations, dissociations, and insensitivities are central to the success of future effective human-machine interaction. Method: Using extant research on the relations between differing methods of workload assessment, factors influencing their association, dissociation, and insensitivity are identified. Results: Dissociations and insensitivities occur more frequently than extant explanatory theories imply. Methodological and conceptual reasons for these patterns of incongruity are identified and evaluated. Application: We often seek convergence of results in order to provide coherent explanations as bases for future prediction and practical design implementation. Identifying and understanding the causes as to why different reflections of workload diverge can help practitioners toward operational success.
... Moreover, the effect of workload on human performance was investigated in many studies [19] [20] [9] [21]. Findings suggest that both very low levels and very high levels of workload can cause human performance degradation. ...
... Moreover, the effect of workload on human performance was investigated in many studies [19] [20] [9] [21]. Findings suggest that both very low levels and very high levels of workload can cause human performance degradation. ...
Preprint
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Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.
... An officer commanding troops understands the importance of effective leadership -but what constitutes leadership of a team of autonomous systems? In some cases, the system may have the capacity to adapt its behavior according to its evaluation of the human's capabilities, i.e., adaptive automation [18]. The downside of this facet of machine intelligence is that the human may feel denigrated if the machine's actions signal that it perceives the human as incompetent. ...
Chapter
Human operators will increasingly team with autonomous systems in military and security settings, for example, evaluation and analysis of threats. Determining whether humans are threatening is a particular challenge to which future autonomous systems may contribute. Optimal trust calibration is critical for mission success, but most trust research has addressed conventional automated systems of limited intelligence. This article identifies multiple factors that may influence trust in autonomous systems. Trust may be undermined by various sources of demand and uncertainty. These include the cognitive demands resulting from the complexity and unpredictability of the system, “social” demands resulting from the system’s capacity to function as a team-member, and self-regulative demands associated with perceived threats to personal competence. It is proposed that existing gaps in trust research may be addressed using simulation methodologies. A simulated environment developed by the research team is described. It represents a “town-clearing” task in which the human operator teams with a robot that can be equipped with various sensors, and software for intelligent analysis of sensor data. The functionality of the simulator is illustrated, together with future research directions.
Chapter
Rapid development of autonomous systems, that act independently and are deeply integrated with humans, necessitates trust-based cooperation and collaboration between these agents and the humans they interact with. A greater understanding of two-way trust between humans and artificial agents is a topic of interest for situations when the human makes mistakes or an anomalous situation such as an enemy combatant taking control of friendly AI. The purpose of this paper is to review the state-of-the-art regarding two-way trust research in Human-Adaptive Agent teams. A systematic review of academic and technical literature from the last ten years (2010–2020) was performed to collect metadata for analysis and discussion. Details of the literature review to include search databases, search terms, and inclusion-exclusion filtering is provided. A metadata analysis is discussed comparing measurements of human trust and agent trust; adaptive-scenario and adaptive-agent mechanisms; type of collaborative human-agent tasking; and level of automation and embodiment of the agent.
Article
Objective This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human–robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Background Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing. Method Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study. Results Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown. Conclusion Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features’ effects. Application Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context.
Article
Background: Several risk factors among packing lines workers can lead to Work-related Musculoskeletal Disorders (WRMSD) occurrence. Foreseeing WRMSD prevention and productivity increase, some furniture manufacturing industries have been investing in the adoption of robotic solutions. In this field, ergonomics plays an important role to verify if automation implementation has been successful. Objective: This study aims to address the general impact and effectiveness from an ergonomics point of view of the implementation of a robotic aid in a packing workstation. Methods: The Nordic Musculoskeletal Questionnaire (NMQ) was applied to 14 workers of semi-automated packing lines. Some additional questions about occupational conditions were included. In order to assess the ergonomic impact of the robotic aid, Rapid Upper Limb Assessment (RULA) was also applied by trained ergonomists, by analyzing the considered packing workstations before and after the adoption of the robotic aid proposed solution. Results: The results showed that trunk torsion was the most highlighted WRMSD risk factor by all workers, associating it with the lumbar pain. The obtained RULA scores demonstrated that the adoption of a robotic aid eliminated this risk factor and, consequently, reduced the corresponding WRMSD risk. Conclusions: The adoption of robotic aids can be instrumental in reducing WRMSD risk in furniture manufacturing industries. Ergonomic studies with workers' participatory approaches seem to be an appropriate strategy to enable the validation and development of industrial robotic solutions.
Article
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Objective: This article advocates multidimensional assessment of task stress in human factors and reviews the use of the Dundee Stress State Questionnaire (DSSQ) for evaluation of systems and operators. Background: Contemporary stress research has progressed from an exclusive focus on environmental stressors to transactional perspectives on the stress process. Performance impacts of stress reflect the operator's dynamic attempts to understand and cope with task demands. Multidimensional stress assessments are necessary to gauge the different forms of system-operator interaction. Method: This review discusses the theoretical and practical use of the DSSQ in evaluating multidimensional patterns of stress response. It presents psychometric evidence for the multidimensional perspective and illustrative profiles of subjective state response to task stressors and environments. Evidence is also presented on stress state correlations with related variables, including personality, stress process measures, psychophysiological response, and objective task performance. Results: Evidence supports the validity of the DSSQ as a task stress measure. Studies of various simulated environments show that different tasks elicit different profiles of stress state response. Operator characteristics such as resilience predict individual differences in state response to stressors. Structural equation modeling may be used to understand performance impacts of stress states. Conclusion: Multidimensional assessment affords insight into the stress process in a variety of human factors contexts. Integrating subjective and psychophysiological assessment is a priority for future research. Application: Stress state measurement contributes to evaluating system design, countermeasures to stress and fatigue, and performance vulnerabilities. It may also support personnel selection and diagnostic monitoring of operators.
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The evolution of robots from tools to teammates requires a paradigm shift. Robot teammates need to interpret naturalistic forms of human communication and sense implicit, but important cues that reflect the human teammate’s psychological state. A closed-loop system where the robot teammate detects the human teammate’s workload state would enable the robot to select appropriate aiding behaviors to support its human teammate. Physiological measures are suitable for assessment of workload in adaptive systems because they allow continuous assessment and do not require overt responses which disrupt tasks. Given the large variability in physiological workload responses across individuals, an algorithm that accommodates variability in workload responses would be more robust. This study outlines the development and validation of algorithms for workload classification. It discusses (i) a workload manipulation paradigm, (ii) the evaluation of the algorithms for deriving a workload index that is individualized, and (iii) parameter selection for optimal classification.
Article
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The implementation of automation relies on the assumption that automation will reduce the operator's cognitive demand and improve performance. However, accepted models demonstrate the multidimensionality of cognitive resources, suggesting that automation must support an appropriate resource dimension to have an appreciable effect. To evaluate this theory, the present study examined the impact of various types of automation on an unmanned ground vehicle (UGV) operator's performance, workload, and stress. The use of a visually demanding task allowed for comparison between an auditory alert (supporting the heavily burdened visual dimension) and a driving aid (supporting action execution, a relatively unburdened cognitive dimension). Static and adaptive (fluctuating based on task demand) levels were implemented for each automation type. Those receiving auditory alerts exhibited better performance and reduced Worry, but also increased Temporal Demand and Effort relative to those receiving driving automation. Adaptive automation reduced workload for those receiving the auditory alerts, and increased workload for those receiving the driving automation. The results from this research demonstrate the need to consider the multidimensionality of the operator's cognitive resources when implementing automation into a system. System designers should consider the type of automation necessary to support the specific cognitive resources burdened by the task.
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A study was run to test the sensitivity of multiple workload indices to the differing cognitive demands of four military monitoring task scenarios and to investigate relationships between indices. Various psychophysiological indices of mental workload exhibit sensitivity to task factors. However, the psychometric properties of multiple indices, including the extent to which they intercorrelate, have not been adequately investigated. One hundred fifty participants performed in four task scenarios based on a simulation of unmanned ground vehicle operation. Scenarios required threat detection and/or change detection. Both single- and dual-task scenarios were used. Workload metrics for each scenario were derived from the electroencephalogram (EEG), electrocardiogram, transcranial Doppler sonography, functional near infrared, and eye tracking. Subjective workload was also assessed. Several metrics showed sensitivity to the differing demands of the four scenarios. Eye fixation duration and the Task Load Index metric derived from EEG were diagnostic of single-versus dual-task performance. Several other metrics differentiated the two single tasks but were less effective in differentiating single- from dual-task performance. Psychometric analyses confirmed the reliability of individual metrics but failed to identify any general workload factor. An analysis of difference scores between low- and high-workload conditions suggested an effort factor defined by heart rate variability and frontal cortex oxygenation. General workload is not well defined psychometrically, although various individual metrics may satisfy conventional criteria for workload assessment. Practitioners should exercise caution in using multiple metrics that may not correspond well, especially at the level of the individual operator.
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Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload 'redlines' which specify when operators are approaching or exceeding their performance tolerances.
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The main methodological drawback to use physiological measures as indicators of arousal is, the large interindividual variability of autonomic responses hindering the direct comparability, between individuals. The present methodology has been tested in two cohorts (n1=910, n2=845) of, pilot applicants during a selection procedure. Physiological data were obtained during two mentally, demanding tasks and during a Flight Simulator Test. Five typical Autonomic Response Patterns (ARP), were identified by cluster analyses. Autonomic spaces were constructed separately for each group of, subjects having the same typical ARP, on the basis of their normalized eigenvectors. The length of the, vector sum of scores on autonomic space dimensions provided an integral index for arousal, labeled, Psychophysiological Arousal Value (PAV). The PAV still reflected the changes in mental load during the, tests, but equalized physiological differences among ARP-groups. The results obtained in the first, cohort were verified in the second cohort.
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In examining the role of time in mental workload, a different perspective is presented from which to view the problem of assessment. Mental workload is plotted in three dimensions, whose axes represent effective rime for action, perceived distance from desired goal state, and level of effort required to achieve the time-constrained goal. This repre-sentation allows the generation of isodynamic workload contours that incorporate the factors of operator skill and equifinality of effort. An adaptive interface for dynamic task reallocation is described that employs this form of assessment to reconcile the joint aims of stable operator loading and acceptable primary task performance by the total system.
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We define situation awareness (SA) as adaptive, externally directed consciousness. This definition dispels the artificial and contentious division evident in the literature, according to which SA is either exclusively knowledge or exclusively process. This misdirected rivalry has more to do with general perspectives on the study of human behavior than with SA itself. Through defining SA as an aspect of consciousness, we hope to clarify two key issues. (1) The source of goals with respect to SA is a normative arbiter in the task environment; that is, the behavior that SA generates must be directed at an external goal. (2) SA is the invariant at the core of the agent's perception-action cycle that supports skilled performance; that is, relationships among factors or dimensions in the environment determine what the agent must know and do to achieve the goals specified by the external arbiter. We introduce a construct we call the risk space to represent the invariant relations in the environment that enable the agent to adapt to novel situations and to attain prespecified goals. We articulate this concept of a risk space through use of a specific example in commercial aircraft operations. The risk space structures information about the physical airspace in a manner that captures the momentary knowledge that drives action and that satisfies the goals and performance criteria for safe and efficient flight. We note that the risk space may be generalized to many different means of navigation.
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Despite the known dangers of driver fatigue, it is a difficult construct to study empirically. Different forms of task-induced fatigue may differ in their effects on driver performance and safety. Desmond and Hancock (2001) defined active and passive fatigue states that reflect different styles of workload regulation. In 2 driving simulator studies we investigated the multidimensional subjective states and safety outcomes associated with active and passive fatigue. Wind gusts were used to induce active fatigue, and full vehicle automation to induce passive fatigue. Drive duration was independently manipulated to track the development of fatigue states over time. Participants were undergraduate students. Study 1 (N = 108) focused on subjective response and associated cognitive stress processes, while Study 2 (N = 168) tested fatigue effects on vehicle control and alertness. In both studies the 2 fatigue manipulations produced different patterns of subjective response reflecting different styles of workload regulation, appraisal, and coping. Active fatigue was associated with distress, overload, and heightened coping efforts, whereas passive fatigue corresponded to large-magnitude declines in task engagement, cognitive underload, and reduced challenge appraisal. Study 2 showed that only passive fatigue reduced alertness, operationalized as speed of braking and steering responses to an emergency event. Passive fatigue also increased crash probability, but did not affect a measure of vehicle control. Findings support theories that see fatigue as an outcome of strategies for managing workload. The distinction between active and passive fatigue is important for assessment of fatigue and for evaluating automated driving systems which may induce dangerous levels of passive fatigue. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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The aim of this study was to integrate empirical data showing the effects of interrupting task modality on the performance of an ongoing visual-manual task and the interrupting task itself. The goal is to support interruption management and the design of multimodal interfaces. Multimodal interfaces have been proposed as a promising means to support interruption management.To ensure the effectiveness of this approach, their design needs to be based on an analysis of empirical data concerning the effectiveness of individual and redundant channels of information presentation. Three meta-analyses were conducted to contrast performance on an ongoing visual task and interrupting tasks as a function of interrupting task modality (auditory vs. tactile, auditory vs. visual, and single modality vs. redundant auditory-visual). In total, 68 studies were included and six moderator variables were considered. The main findings from the meta-analyses are that response times are faster for tactile interrupting tasks in case of low-urgency messages.Accuracy is higher with tactile interrupting tasks for low-complexity signals but higher with auditory interrupting tasks for high-complexity signals. Redundant auditory-visual combinations are preferable for communication tasks during high workload and with a small visual angle of separation. The three meta-analyses contribute to the knowledge base in multimodal information processing and design. They highlight the importance of moderator variables in predicting the effects of interruption task modality on ongoing and interrupting task performance. The findings from this research will help inform the design of multimodal interfaces in data-rich, event-driven domains.
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The out-of-the-loop performance problem, a major potential consequence of automation, leaves operators of automated systems handicapped in their ability to take over manual operations in the event of automation failure. This is attributed to a possible loss of skills and of situation awareness (SA) arising from vigilance and complacency problems, a shift from active to passive information processing, and change in feedback provided to the operator. We studied the automation of a navigation task using an expert system and demonstrated that low SA corresponded with out-of-the-loop performance decrements in decision time following a failure of the expert system. Level of operator control in interacting with automation is a major factor in moderating this loss of SA. Results indicated that the shift from active to passive processing was most likely responsible for decreased SA under automated conditions.
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The intent of this paper is to provide the reader with an overview of the mental workload literature. It will focus on other state-of-the-art surveys with reference to some specific reports of the practical application of mental workload measurement. The surveys will be limited to those in English. Manzey reportedly provides a review of psycho-physiological methods in German; a comparable, recent review in English was not found although a NATO RTO report (Wilson 2004, pp. 64-65 and Chapter 8) provides some guidance in this respect. Appendix 1 lists a search for references to workload measurement techniques using the GOOGLE search engine. The intent is to give the reader an appreciation of where work has been focused, or at least as reported on the Internet.
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the question of mental workload (MWL) assessment is relatively new and important; new in comparison to companion techniques for the assessment of physical load, whose origins are the contemporary of the Industrial Revolution, and important in that an increasing proportion of work taxes the information processing capabilities of operators, rather than their physical capacity / it is the load placed upon such cognitive capabilities that mental workload assessment is designed to measure / the techniques used to measure this load are the primary focus of this chapter / 4 contemporary groups of methods, each comprising several techniques, are evaluated . . . from the view point of their practicality and utility for the working ergonomist: [primary task measures, secondary task measures, subjective rating measures, and physiological (or psychophysiological) measures] (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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We present a study of people's attitudes toward robot workers, identifying the characteristics of occupations for which people believe robots are qualified and desired. We deployed a web-based public-opinion survey that asked respondents (n=250) about their attitudes regarding robots' suitability for a variety of jobs (n=812) from the U.S. Department of Labor's O*NET occupational information database. We found that public opinion favors robots for jobs that require memorization, keen perceptual abilities, and service-orientation. People are preferred for occupations that require artistry, evaluation, judgment and diplomacy. In addition, we found that people will feel more positively toward robots doing jobs with people rather than in place of people.
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Arguments are presented that an integrated view of stress and performance must consider the task demanding a sustained attention as a primary source of cognitive stress. A dynamic model is developed on the basis of the concept of adaptability in both physiological and psychological terms, that addresses the effects of stress on vigilance and, potentially, a wide variety of attention-demanding performance tasks. The model provides an insight into the failure of an operator under the driving influences of stress and opens a number of potential avenues through which solutions to the complex challenge of stress and performance might be posed.
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The authors discuss empirical studies of human-automation interaction and their implications for automation design. Automation is prevalent in safety-critical systems and increasingly in everyday life. Many studies of human performance in automated systems have been conducted over the past 30 years. Developments in three areas are examined: levels and stages of automation, reliance on and compliance with automation, and adaptive automation. Automation applied to information analysis or decision-making functions leads to differential system performance benefits and costs that must be considered in choosing appropriate levels and stages of automation. Human user dependence on automated alerts and advisories reflects two components of operator trust, reliance and compliance, which are in turn determined by the threshold designers use to balance automation misses and false alarms. Finally, adaptive automation can provide additional benefits in balancing workload and maintaining the user's situation awareness, although more research is required to identify when adaptation should be user controlled or system driven. The past three decades of empirical research on humans and automation has provided a strong science base that can be used to guide the design of automated systems. This research can be applied to most current and future automated systems.
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The objective is to lay out the rationale for multiple resource theory and the particular 4-D multiple resource model, as well as to show how the model is useful both as a design tool and as a means of predicting multitask workload overload. I describe the discoveries and developments regarding multiple resource theory that have emerged over the past 50 years that contribute to performance and workload prediction. The article presents a history of the multiple resource concept, a computational version of the multiple resource model applied to multitask driving simulation data, and the relation of multiple resources to workload. Research revealed the importance of the four dimensions in accounting for task interference and the association of resources with brain structure. Multiple resource models yielded high correlations between model predictions and data. Lower correlations also identified the existence of additional resources. The model was shown to be partially relevant to the concept of mental workload, with greatest relevance to performance breakdowns related to dual-task overload. Future challenges are identified. The most important application of the multiple resource model is to recommend design changes when conditions of multitask resource overload exist.
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Studies reporting physiological responses as reflections of mental workload are reviewed briefly. The differing measures are located in a two-dimensional space whose axes represent first, practical application and second, relevance to actual central nervous system activity as viewed from spatial and systemic congruence. Traditional methods such as those using heart rate are identified as the most practical current measures, while evoked cortical potentials emerge as superior upon the latter axis. The potential of auditory canal temperature as an optimal composite measure is explored.
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The present study investigated how three task demand factors influenced performance, subjective workload and stress of novice intelligence, surveillance, and reconnaissance operators within a simulation of an unmanned ground vehicle. Manipulations were task type, dual-tasking and event rate. Participants were required to discriminate human targets within a street scene from a direct video feed (threat detection [TD] task) and detect changes in symbols presented in a map display (change detection [CD] task). Dual-tasking elevated workload and distress, and impaired performance for both tasks. However, with increasing event rate, CD task deteriorated, but TD improved. Thus, standard workload models provide a better guide to evaluating the demands of abstract symbols than to processing realistic human characters. Assessment of stress and workload may be especially important in the design and evaluation of systems in which human character critical signals must be detected in video images.
Chapter
Many test environments have been constructed to support experiments with human participants over the years. Some, such as the Multi-Attribute Task Battery (MATB) and Virtual Battlefield System 2 (VBS2), are used widely throughout the research community. However, each is limited, particularly in their integration, or lack thereof, of physiological measures. The Mixed Initiative Experimental (MIX) testbed was constructed to capitalize on the benefits afforded by MATB and VBS2 and address the weaknesses present in those test environments. Theory driven tasks, integrated physiological and logging capabilities, and an easily reconfigurable interface define the MIX testbed. For those reasons, the MIX testbed is proposed as the next generation approach for testing environments.
Chapter
Four primary methods of mental workload assessment, i.e., Secondary Task, Subjective Rating, Performance Measure, and Physiological are reviewed and the latest development in each one is also evaluated. Furthermore, based upon a thorough, critical analysis, it is found that all of the methods are very sensitive to the effects of the individual differences factor. Therefore, it is recommended that, in order to develop a comprehensive conceptual paradigm for mental workload measurement, the factor of individual differences in information processing should not only be incorporated in the model, but also be regarded as one of the promising areas for further research.
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In this chapter, we argue that adaptive performance situations will vary in terms of their cognitive, social, and emotional loads. Accordingly, success in such situations will require varying degrees of not just cognitive readiness but social and emotional readiness as well. Cognitive, social, and emotional elements of adaptive readiness reflect different sets of knowledge, skills, and abilities (KSAs) and competencies. Overall adaptive readiness will depend upon the combination of KSAs that corresponds to the load mix in a particular situation. Accordingly, in a situation with high cognitive social and emotional load, cognitive readiness will not be enough to ensure overall operational effectiveness. Finally, different training strategies will be needed to foster cognitive, social, and emotional readiness. We describe some of the KSAs associated, respectively, with cognitive, social, and emotional readiness, along with corresponding training strategies. © 2014 Springer Science+Business Media New York. All rights are reserved.
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Future robots may act as team members, capable of independently executing tasks and interacting with humans cooperatively and socially. These types of robots will occupy a niche between what is currently addressed by human-robot interaction (HRI) and what is considered to be teamwork. This paper addresses that need by providing innovative direction in the measurement of situation awareness (SA) while considering robotic agents as integral team members. We include relevant metrics for the measurement of individual SA within human-robot teams and present a model describing SA of the team. In doing this, we expand upon current methodologies for measuring SA so that they can be applied to future human-robot teams.
Conference Paper
This chapter discusses contemporary advances in the understanding of adaptive control as applied to systems that include the cooperative action of a machine and its operator. The key component of an adaptive interface is a reasoning process that selects a task allocation policy that changes the loading on the human in such a way as to improve overall system performance. This process must have access to both overall system goals (a model of the task) and information about what the person and machine components of the system are capable of accomplishing (person and system models). As an initial foundation, it is recognized in the chapter that the prosthetics that can surround individuals and augment their capabilities allow human operators to traverse the traditional boundary constraints imposed by the environment. A different approach is advocated where a hierarchical model of the task is built in terms of procedural and knowledge-based components. The human–machine interaction is a view of the task as a knowledge system that requires combined human–machine intelligence along with an interface that permits and controls joint human–machine reasoning. The chapter describes some developments in the understanding of human-adaptive response and the way by which such adaptive capability may be replicated in human–machine systems.
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The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human–agent (H-A) interaction, and retention of human decision authority. A number of approaches—from flexible automation to autonomous agents—were reviewed, and their advantages and disadvantages were discussed. In addition, two key human performance issues (trust and situation awareness) related to H–A teaming for multirobot control and some promising user interface design solutions to address these issues were discussed. Some major individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.
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BORGHINI et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness NEUROSCI BIOBEHAV REV 21(1) XXX-XXX, 2012. This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggest that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterize the transition between mental workload and mental fatigue. Drowsiness is also characterized by increased blink rate and decreased HR values. The detection of such mental states is actually performed "off-line" with accuracy around 90% but not on-line. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented.
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This paper presents a cognitive-energetical framework for the analysis of effects of stress and high workload on human performance. Following Kahneman's (1973) model, regulation of goals and actions is assumed to require the operation of a compensatory control mechanism, which allocates resources dynamically. A two-level compensatory control model provides the basis for a mechanism of resource allocation through an effort monitor, sensitive to changes in the level of regulatory activity, coupled with a supervisory controller which can implement different modes of performance-cost trade-off. Performance may be protected under stress by the recruitment of further resources, but only at the expense of increased subjective effort, and behavioural and physiological costs. Alternatively, stability can be achieved by reducing performance goals, without further costs. Predictions about patterns of latent decrement under performance protection are evaluated in relation to the human performance literature. Even where no primary task decrements may be detected, performance may show disruption of subsidiary activities or the use of less efficient strategies, as well as increased psychophysiological activation, strain, and fatigue after-effects. Finally, the paper discusses implications of the model for the assessment of work strain, with a focus on individual-level patterns of regulatory activity and coping.
Chapter
The results of a multi-year research program to identify the factors associated with variations in subjective workload within and between different types of tasks are reviewed. Subjective evaluations of 10 workload-related factors were obtained from 16 different experiments. The experimental tasks included simple cognitive and manual control tasks, complex laboratory and supervisory control tasks, and aircraft simulation. Task-, behavior-, and subject-related correlates of subjective workload experiences varied as a function of difficulty manipulations within experiments, different sources of workload between experiments, and individual differences in workload definition. A multi-dimensional rating scale is proposed in which information about the magnitude and sources of six workload-related factors are combined to derive a sensitive and reliable estimate of workload.
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The current study measured how concurrent driving and in-vehicle activities of different levels of engagement varied in terms of performance and subjective estimates of demand and performance. In this test track study, 41 younger and older drivers completed a series of cognitive tasks while driving an instrumented vehicle. One task involved an engaging guessing game where drivers tried to guess the identity of an object. The other task involved a simple mental arithmetic task. We observed some dissociation between drivers' performance and their subjective reports. For instance, drivers tended to estimate their performance as better for the more engaging guessing task than the arithmetic task, though their performance was actually worse. At the same time, subjective estimates of workload across the two tasks did not vary in the dual-task condition even though they did in the single-task baseline conditions, suggesting that drivers failed to account for the added demands in dual-task situations. We discuss the implications of these findings for driver safety. Crashes due to distraction can carry tremendous costs for employers, in terms of injury, disability, and loss of potentially productive work years, whether these crashes occur on or off the job.
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The need to assess pilot workload during flight has become of increasing importance over the past decade--both in combat aircraft where workload can be excessive, and in civil transport aircraft where underload may occur. Subjective reporting in some form has been the long and well established method for assessing workload in the 'real world'. but because subjective opinions are vulnerable to bias and to preconceived notions, an additional measure can be of considerable value on occasions. Of the available psychophysiological variables, recording pilots' heart rates appears to be the most useful. This paper discusses the choice of this measure and presents examples from several studies in which workload was assessed in flight.
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Adaptive automation is an approach to automation design where tasks are dynamically allocated between the human operator and computer systems. Psychophysiology has two complementary roles in research on adaptive automation: first, to provide information about the effects of different forms of automation thus promoting the development of effective adaptive logic; and second, psychophysiology may yield information about the operator that can be integrated with performance measurement and operator modelling to aid in the regulation of automation. This review discusses the basic tenets of adaptive automation and the role of psychophysiological measures in the study of adaptive automation. Empirical results from studies of flight simulation are presented. Psychophysiological measures may prove especially useful in the prevention of performance deterioration in underload conditions that may accompany automation. Individual differences and the potential for learned responses require research to understand their influence on adaptive algorithms. Adaptive automation represents a unique domain for the application of psychophysiology in the work environment.
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Previous research has found that vehicle automation systems can reduce driver mental workload, with implications for attentional resources that can be detrimental to performance. The present paper considers how the development of automaticity within the driving task may influence performance in underload situations. Driver skill and vehicle automation were manipulated in a driving simulator, with four levels of each variable. Mental workload was assessed using a secondary task measure and eye movements were recorded to infer attentional capacity. The effects of automation on driver mental workload were quite robust across skill levels, but the most intriguing findings were from the eye movement data. It was found that, with little exception, attentional capacity and mental workload were directly related at all levels of driver skill, consistent with earlier studies. The results are discussed with reference to applied theories of cognition and the design of automation.
Investigating the Universality and Comprehensive Ability of Measures to Assess the State of Workload
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