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Human-Swarm Interaction: Effects on Operator Workload, Scale, and Swarm TopologyPendleton, Brian O. 04 September 2013 (has links) (PDF)
Robots, including UAVs, have found increasing use in helping humans with dangerous and difficult tasks. The number of robots in use is increasing and is likely to continue increasing in the future. As the number of robots increases, human operators will need to coordinate and control the actions of large teams of robots. While multi-robot supervisory control has been widely studied, it requires that an operator divide his or her attention between robots. Consequently, the use of multi-robot supervisory control is limited by the number of robots that a human or team of humans can reasonably control. Swarm robotics -- large numbers of low-cost robots displaying collective behaviors -- offers an alternative approach by providing the operator with a small set of inputs and parameters that alter the behavior of a large number of autonomous or semi-autonomous robots. Researchers have asserted that this approach is more scalable and offers greater promise for managing huge numbers of robots. The emerging field of Human-Swarm Interaction (HSI) deals with the effective management of swarms by human operators. In this thesis we offer foundational work on the effect of HSI (a) on the individual robots, (b) on the group as a whole, and (c) on the workload of the human operator. We (1) show that existing general swarm algorithms are feasible on existing robots and can display collective behaviors as shown in simulations in the literature, (2) analyze the effect of interaction style and neighborhood type on the swarm's topology, (3) demonstrate that operator workload stays stable as the size of the swarm increases, but (4) find that operator workload is influenced by the interaction style. We also present considerations for swarm deployment on real robots.
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Evaluating Mental Workload for AR Head-Mounted Display Use in Construction Assembly TasksQin, Yimin 14 June 2023 (has links)
Augmented Reality (AR) head-mounted display (HMD) provides users with an immersive virtual experience in the real world. The portability of this technology affords various information display options for construction workers that are not possible otherwise. The information delivered via an interactive user interface provides an innovative method to display complex building instructions, which is more intuitive and accessible compared with traditional paper documentations. However, there are still challenges hindering the practical usage of this technology at the construction jobsite. As a technical restriction, current AR HMD products have a limited field of view (FOV) compared to the human vision range. It leads to an uncertainty of how the obstructed view of display will affect construction workers' perception of hazards in their surrounding area. Similarly, the information displayed to workers requires rigorous testing and evaluation to make sure that it does not lead to information overload. Therefore, it is essential to comprehensively evaluate the impacts of using AR HMD from both perspectives of task performance and cognitive performance. This dissertation aims to bridge the gap in understanding the cognitive impacts of using AR HMD in construction assembly tasks. Specifically, it focuses on answering the following two questions: (1) How are task performance and cognitive skills affected by AR displays under complex working conditions? (2) How are moment-to-moment changes of mental workload captured and evaluated during construction assembly tasks?
To answer these questions, this dissertation proposed two experiments. The first study tests two AR displays (conformal and tag-along) and paper instruction under complex working conditions, involving different framing scales and interference settings. Subjective responses are collected and analyzed to evaluate overall mental workload and situation awareness. The second study focuses on exploring an electroencephalogram (EEG) based approach for moment-to-moment capture and evaluation of mental workload. It uncovers the cognitive change on the time domain and provides room for further quantitative analyzing on mental workload. Especially, two frameworks of mental workload prediction are proposed by using (1) Long Short-Term Memory (LSTM) and (2) one-dimensional Convolutional Neural Network (1D CNN)-LSTM for forecasting EEG signal and, classifying task conditions and mental workload levels respectively. The approaches are tested to be effective and reliable for predicting and recognizing subjects' mental workload during assembly. In brief, this research contributes to the existing knowledge with an assessment of AR HMD use in construction assembly, including task performance evaluation and both subjective and physiological measurements for cognitive skills. / Doctor of Philosophy / Augmented Reality (AR) is an emerging technology that bridges the gap between virtual creatures and physical world with an immersive display experience. Today, head-mounted display (HMD) is well developed to meet the demands for portable AR devices. It provides interactive and intuitive display of 2D graphical information to make it easier to understand for users. Therefore, AR display has been studied in the past few years for a more simplified and productive construction assembly process. However, given the premise that construction is a high-risk industry, introducing such display technology to the jobsite needs to be carefully tested. One obstacle in current AR HMD products is the restriction of field of view (FOV), which may block users' view in presenting large-scale 3D objects. In construction assembly, workers need to deal with tasks in different scopes, such as wood framing for a residential house. Consequently, it is necessary to study how such technical challenge will impact workers' performance under different task conditions. Another concern comes from the mental perspective. Although AR display may bring convenience in acquiring effective information, it is difficult to measure if this generates excessive mental burden to users. Especially for construction workers, whether the overlaid display will cause distraction and information overload is crucial for protecting workers from hazards.
To address the problems, this dissertation explores the gap in previous literature, where mental workload is not well studied for using AR HMD in construction assembly. Two experiments are conducted to comprehensively evaluate the impacts of AR displays on both assembly performance and users' mental status. The outcomes bring implications to theoretical and practical aspects. First, it compares two AR displays (2D tag-along image and 3D conformal model) with traditional paper documentation for assembly performance (efficiency and accuracy) and users' cognitive skills (mental workload and situation awareness). The findings revealed the impact of FOV restriction and provided a strategic solution to selecting display method for different task conditions. Second, it proposes a physiological approach to calculate mental workload from analyzing the features from brain waves. It uncovered the latent mental changes during the assembly. Furthermore, two deep learning approaches are applied to predict and classify mental workload. The prediction model depicted the trend of mental workload in eighteen seconds based on an eighty-four-second training set, while the classifier recognized two task conditions with different mental workload levels with an accuracy of 93.6%. The results have promising potential for future research in detecting and preventing abnormality in workers' mental status. In addition, it is generalizable to apply in other construction tasks and AR applications.
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A systematic investigation of EEG and fNIRS measures for the assessment of mental workload in the cockpitHamann, Anneke 28 August 2023 (has links)
Assessing the pilot’s cognitive state is of increasing importance in aviation, especially for the development of adaptive assistance systems. For this purpose, the assessment of mental workload (MWL) is of special interest as an indication when and how to adapt the automation to fit the pilot’s current needs. Thus, there is a need to assess the pilot continuously, objectively and non-intrusively. Neurophysiological measurements like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are promising candidates for such an assessment. Yet, there is evidence that EEG- and fNIRS-based MWL measures are susceptible to influences from other concepts like mental fatigue (MF), and decrease in accuracy when MWL and MF confound. Still, there are only few studies targeting this problem, and no systematic investigation into this problem has taken place. Thus, the validity of neurophysiological MWL measures is not clear yet.
In order to undertake such a systematic investigation, I conducted three studies: one experiment in which I investigated the effects of increasing MWL on cortical activation when MF is controlled for; a second experiment in which I examined the effects of increasing MF on cortical activation when MWL is controlled for; and a further comparative analysis of the gathered data. In order to induce MWL and MF in a controllable and comparable fashion, I conceived and used a simplified simulated flight task with an incorporated adapted n-back and monitoring task. I used a concurrent EEG-fNIRS measurement to gain neurophysiological data, and collected performance data and self-reported MWL and MF. In the first study (N = 35), I induce different four levels of MWL by increasing the difficulty of the n-back task, and controlled for MF by means of randomization and a short task duration (≤ 45 minutes). Higher task difficulty elicited higher subjective MWL ratings, declining performance, increased frontal theta band power and decreased frontal deoxyhaemoglobin (HbR) concentration. Furthermore, fNIRS proved more sensitive to tasks with low difficulty, and EEG to tasks with high difficulty. Only the combination of both methods was able to discriminate all four induced MWL levels. Thus, frontal theta band power and HbR were sensitive to changing MWL. In the second study (N = 31), I. I induced MF by means of time on task. Thus, I prolonged the task duration to approx. 90 minutes, and controlled for MWL by using a low but constant task difficulty derived from the first experiment. Over the course of the experiment, the participants’ subjective MF increased linearly, but their performance remained stable. In the EEG data, there was an early increase and levelling in parietal alpha band power and a slower, but steady increase in frontal theta band power. The fNIRS data did not show a consistent trend in any direction with increasing MF. Thus, only parietal alpha and frontal theta band power were sensitive to changing MF. In the third study, I investigated the validity of two EEG indices commonly used for MWL assessment, the Task Load Index (TLI) and the Engagement Index (EI). I computed the indices from the data of the two experiments, and compared the results between the datasets, and to single band powers. The TLI increased with increasing MWL, but was less sensitive than theta band power alone, and varied slightly with increasing MF. The EI did not vary with MWL, and was not sensitive to gradually increasing MF. Thus, neither index could be considered a valid MWL measure.
In sum, neurophysiological measures can be used to assess changes in MWL. Yet, frontal HbR was the only measure sensitive to MWL that did not also vary with MF, and further research is needed to conclude if this finding holds true under different task characteristics. Thus, the tested EEG and fNIRS measures are only valid indications of MWL when confounding effects of MF are explicitly controlled for. I discuss further influences on the tested EEG and fNIRS measures, possible combinations with other data sources, and practical challenges for a neurophysiological MWL assessment. I conclude that neurophysiological measures should be used carefully outside the laboratory, as their validity will likely suffer in realistic settings. When their limitations are understood and respected, they can help to understand the cognitive processes involved in MWL, and can be a valuable addition to an MWL assessment.
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Quantifying cognitive workload and defining training time requirements using thermographyKang, Jihun 13 December 2008 (has links)
Effective mental workload measurement is critical because mental workload significantly affects human performance. A non-invasive and objective workload measurement tool is needed to overcome limitations of current mental workload measures. Further, training/learning increases mental workload during skill or knowledge acquisition, followed by a decreased mental workload, though sufficient training times are unknown. The objectives of this study were to: (1) investigate the efficacy of using thermography as a non-contact physiological measure to quantify mental workload, (2) quantify and describe the relationship between mental workload and learning/training, and, (3) introduce a method to determine a sufficient training time and an optimal human performance level for a novel task by using thermography. Three studies were conducted to address these objectives. The first study investigated the efficacy of using thermography to quantity the relationship between mental workload and facial temperature changes while learning an alpha-numeric task. Thermography measured and quantified the mental workload level successfully. Strong and significant correlations were found among thermography, performance, and subjective workload measures (MCH and SWAT ratings). The second study investigated the utility of using a psychophysical approach to determine workload levels that maximize performance on a cognitive task. The second study consisted of an adjustment session (participants adjusted their own workload levels) and work session (participants worked at the chosen workload level). Participants were found to fall into two performance groups (low and high performers by accuracy rate) and results were significantly different. Thermography demonstrated whether both group found their optimal workload level. The last study investigated efficacy of using thermography to quantify mental workload level in a complex training/learning environment. Experienced drivers’ performance data was used as criteria to indicate whether novice drivers mastered the driving skills. Strong and significant correlations were found among thermography, subjective workload measures, and performance measures in novice drivers. This study verified that thermography is a reliable and valid way to measure workload as a non-invasive and objective method. Also, thermography provided more practical results than subjective workload measures for simple and complex cognitive tasks. Thermography showed the capability to identify a sufficient training time for simple or complex cognitive tasks.
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Prediction of Pilot Skill Level and Workload for Sliding-Scale Autonomous SystemsNittala, Sai Kameshwar Rao January 2017 (has links)
No description available.
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Evaluation of Consumer Drone Control InterfaceMerrell, Thomas William, Jr. 16 May 2018 (has links)
No description available.
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Impact of Noise Level on Task Performance and Workload and Correlation to PersonalityEakins, Kaylee Marie 06 June 2018 (has links)
No description available.
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Demand on Mental Workload: Relation to Cue Reactivity and Craving in Women with Disordered Eating and Problematic DrinkingRofey, Dana Lynn 30 September 2005 (has links)
No description available.
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A Theoretical Framework For Evaluating Mental Workload Resources in Human Systems Design for Manufacturing OperationsBommer, Sharon Claxton 31 May 2016 (has links)
No description available.
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Active Noise Reduction Versus Passive Designs in Communication Headsets: Speech Intelligibility and Pilot Performance Effects in an Instrument Flight SimulationValimont, Robert Brian 08 May 2006 (has links)
Researchers have long known that general aviation (GA) aircraft exhibit some of the most intense and potentially damaging sound environments to a pilot's hearing. Yet, another potentially more ominous result of this noise-intense environment is the masking of the radio communications. Radio communications must remain intelligible, as they are imperative to the safe and efficient functioning of the airspace, especially the airspace surrounding our busiest airports, Class B and Class C. However, the high amplitude, low frequency noise dominating the GA cockpit causes an upward spreading of masking with such inference that it renders radio communications almost totally unintelligible, unless the pilot is wearing a communications headset. Even with a headset, some researchers have stated that the noise and masking effects overcome the headset performance and still threaten the pilot's hearing and overall safety while in the aircraft.
In reaction to this situation, this experiment sought to investigate the effects which active noise reduction (ANR) headsets have on the permissible exposure levels (PELs), speech intelligibility, workload, and ultimately the pilot's performance inside the cockpit. Eight instrument-rated pilot participants flew through different flight tasks of varying levels and types of workload embedded in four 3.5 hour flight scenarios while wearing four different headsets. The 3.5 hours were considered long duration due the instrument conditions, severe weather conditions, difficult flight tasks, and the fatiguing effects of a high intensity noise environment. The noise intensity and spectrum in the simulator facility were specifically calibrated to mimic those of a Cessna 172. Speech intelligibility of radio communications was modified using the Speech Transmission Index (STI), while measures of flight performance and workload were collected to examine any relationships between workload, speech intelligibility, performance, and type of headset.
It is believed that the low frequency attenuation advantages afforded by the ANR headset decreased the signal-to-noise ratio, thereby increasing speech intelligibility for the pilot. This increase may positively affect workload and flight performance. Estimates of subjective preference and comfort were also collected and analyzed for relevant relationships.
The results of the experiment supported the above hypotheses. It was found that headsets which incorporate ANR technology do increase speech intelligibility which has a direct inverse influence on workload. For example, an increase in speech intelligibility is seen with a concomitant decrease in pilot workload across all types and levels of workload. Furthermore, flight task performance results show that the pilot's headset can facilitate safer flight performance. However, the factors that influence performance are more numerous and complex than those that affect speech intelligibility or workload. Factors such as the operational performance of the communications system in the headset, in addition to the ANR technology, were determined to be highly influential factors in pilot performance.
This study has concluded that the pilot's headset has received much research and design attention as a noise attenuation device. However, it has been almost completely overlooked as a tool which could be used to facilitate the safety and performance of a general aviation flight. More research should focus on identifying and optimizing the headset components which contribute most to the results demonstrated in this experiment. The pilot's headset is a component of the aviation system which could economically improve the safety of the entire system. / Ph. D.
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