With the development of technology, people's viewpoints of the automobile have shifted; instead of merely a means of transportation, the automobile has become a space in which a driver can still perform daily activities besides driving, such as communicating with other people, interacting with electronic devices, and receiving information. In the meantime, different ways of interaction have been explored. Among all the modalities, gestures have been considered as a feasible way for performing in-car secondary tasks because of their intuitiveness. However, few researches have been conducted in terms of subjects' cognitive load. This thesis has examined four gesture interfaces (air swipe, air tap, screen swipe, and screen tap), in terms of their effects on drivers' driving performance, secondary task performance, perceived cognitive load, and eye glance behavior. The result demonstrated that air gestures are generally slower than screen gestures with regard to secondary performance. Screen swipe gesture requires the lowest cognitive load while air swipe and screen tap gesture remain the same. Subjects in this study tend to prefer screen swipe gesture the most while prefer air tap gesture the least. However, there is no significant difference between air swipe and screen tap gesture. Although air tap gesture and screen tap gesture generated the largest amount of dwell times, no variance among the four gesture interfaces in driving performance has been found. The result indicated that even though air gestures are not limited by space, screen swipe in this study still seemed to be the most ideal way for performing in-car secondary task of music selection.

The e-OPRA Implant System (Integrum AB, Sweden) is a system which employs permanently accessible implantable neuromuscular electrodes in combination with osseointegrated attachment of the prosthesis to the skeleton, in order to create a more natural control of advanced robotic upper-limb prostheses. The system enables the possibility of sensory feedback, via a cuff electrode to the ulnar nerve which allows for direct neurostimulation of the nerve. This work proposes a method using electroencephalography (EEG) to quantitatively evaluate the cognitive workload of a person controlling a prosthesis, and how said workload changes when sensory feedback is enabled. Based on previous studies on EEG and cognitive workload, the proposed methods include collecting EEG data from subjects who are performing a grasping task while listening to a selection of sounds and counting the number of times a specific tone is presented. The data is analysed using both event related potentials (ERPs) as well as spectral analysis. The method was used in a trial run consisting of two healthy subjects, and one transhumeral amputee implanted with the e-OPRA system. Although the subject group was not large enough to draw any statistical conclusions, the trial run and the results from it suggest that the methods could be used in a larger study to evaluate the cognitive workload of amputees implanted with the e-OPRA system.

The aim of this PhD thesis was to investigate traffic safety and mobility for individuals with hearing loss (HL). Three studies were conducted: 1. a questionnaire survey aimed to evaluate differences in choice of transportation that might be related to HL, 2. a driving simulator study that looked into compensatory strategies and evaluated the efficiency of a tactile signal to alert the driver, and 3. a field study to evaluate these effects in real traffic and to evaluate a navigation system with a supportive tactile signal. The effects of HL discovered in this thesis add to the knowledge and understanding of the influence of HL on traffic safety and mobility. Differences found consistently point to a generally more cautious behavior. Compensatory and coping strategies associated with HL are bound to driving complexity and appear when complexity increases. These strategies include driving at lower speeds, using a more comprehensive visual search behavior and being less engaged in distracting activities. Evaluation of a tactile signal showed that by adding a tactile modality, some driver assistance systems can also be made accessible to drivers with HL. At the same time, the systems might be more effective for all users, since the driver can be more focused on the road. Based on the results in this thesis, drivers with HL cannot be considered an increased traffic safety risk, and there should be no need for adjustments of the requirements of hearing for a license to drive a car. / Syftet med den här doktorsavhandlingen var att undersöka trafiksäkerhet och mobilitet för individer med hörselnedsättning (HN). Tre studier har genomförts: 1. en enkätstudie för att undersöka skillnader i transportvanor relaterade till HN, 2. en körsimulatorstudie for att titta på kompensatoriska strategier och utvärdera effektiviteten i en taktil signal för att påkalla förarens uppmärksamhet och 3. en fältstudie för att undersöka effekterna i riktig trafik samt utvärdera ett navigationssystem med en taktil signal som stöd för navigering. Effekterna av HN som kom fram i denna avhandling bidrar till kunskapen och förståelsen för hur HN påverkar trafiksäkerhet och mobilitet. De funna skillnaderna pekar konsistent mot ett generelltmera försiktigt beteende. Kompensatoriska - och copingstrategier förknippade med HN beror på körkomplexitet och observeras när komplexiteten ökar. Dessa strategier innebär körning med lägre hastighet, mera heltäckande visuell avsökning och mindre engagemang i distraherande uppgifter. Utvärdering av en taktil signal visade att genom att lägga till en taktil modalitet kan vissa förarstödsystem bli tillgängliga även för förare med HN. Samtidigt kan systemen bli mera effektiva för alla användare eftersom föraren då kan fokusera mera på vägen. Baserat på resultaten i den här avhandlingen kan inte förare med HN betraktas som någon förhöjd risk och det bör därmed inte finnas något behov av att justera hörselkraven när det gäller körkortsinnehav.

Αντικείμενο της παρούσας Διπλωματικής εργασίας είναι η σχεδίαση και ανάπτυξη εργαλείου εξόρυξης γνώσης για τη συναισθηματική κατάσταση χρηστών από δεδομένα συσκευής Eye tracking, όπως επίσης και η μελέτη της μεταβολής γνωσιακού φορτίου χρηστών με τη βοήθεια πειραματικής διαδικασίας. Στη Διπλωματική αυτή εργασία σχεδιάστηκε και αναπτύχθηκε μια εφαρμογή που εκτελεί την παραπάνω λειτουργία, και διεξήχθη ένα πείραμα με σκοπό να συσχετιστούν συγκεκριμένες μετρικές Eye tracking με τη συναισθηματική κατάσταση και το γνωσιακό φορτίο χρηστών. / Design and developpment of a data-mining tool, for the cognitive workload of users from the data of an eye-tracking device.

<p>  </p> <p>Automation, as defined by Parasuraman et al. (2000, p. 287), is a “device or system that either partially or fully, accomplishes a function that was previously, partially, or fully accomplished by a human operator.” Traditionally, automation was introduced to (physical) work environments to alleviate workload associated with tedious and repetitive tasks. Over the past few decades, automation has begun to augment knowledge work, which includes high-level cognitive activities.  As automated systems expand to perform skill-based tasks, the work required of humans is inevitably altered, potentially affecting their cognitive workloads. Years of research has shown that automation can reduce cognitive workload, but other work suggests that cognitive workload may increase or remain unchanged when automation is introduced. These conflicting results prompt the need for further investigation to better delineate the relationship between automation and cognitive workload. </p> <p>A plethora of factors may explain why the relationship between automation and cognitive workload is inconsistent. Therefore, this research takes steps toward addressing knowledge gaps within the human-automation interaction literature related to understanding how automation used in knowledge work environments affects peoples’ task completion. Specifically, this work investigates how two moderators, task complexity and age, influence the automation and cognitive workload relationship. These moderators were of interest for two reasons. First, task complexity, which occurs when the structure of a task imposes demands on a person’s cognitive processes, increases the demands of a task, which can result in the use of more cognitive resources. Second, age is of interest because advanced technologies are increasingly being utilized by a wide user demographic, particularly the rapidly-growing older adult population.  </p> <p>The goals of this dissertation were achieved by employing both qualitative and quantitative methods to examine how (1) automation is assessed in knowledge work environments, (2) automation affects cognitive workload, and (3) task complexity and age moderate the relationship between automation and cognitive workload. These goals were first addressed via the construction of a conceptual framework that describes the effects that task complexity and age have on the relationship between automation and cognitive workload. Next, a systematic review of the human-automation interaction literature in knowledge work environments was performed to examine researchers’ use of cognitive workload measures. Finally, a controlled-laboratory experiment and a scenario-based survey were conducted to collect data from people of different ages about how task complexity and age influence the relationship between automation and cognitive workload. </p> <p>Findings from the systematic literature review showed that researchers primarily employ subjective and performance measures to assess cognitive workload. Results from the laboratory experiment suggested that automation improved measures of cognitive workload. Also, task complexity negatively affected the relationship between automation and cognitive workload, but age was not found to be a moderator. The scenario-based survey revealed that task performance was similar among younger, middle-aged, and older adults. However, younger adults had a more favorable opinion of automation compared to both middle-aged and older age groups. </p> <p>Overall, this research (1) enhances our knowledge of the relationship between automation and cognitive workload, (2) informs the design of future human-automation studies with strategically selected task types and measurement choices, based on patterns that emerged from the literature review, and (3) can ultimately guide designers in better developing technologies to support people in performing various activities in their work and leisure environments. </p>

In human-computer systems, such as supervisory control systems, large volumes of incoming and complex information can degrade overall system performance. Strategically integrating automation to offload tasks from the operator has been shown to increase not only human performance but also operator efficiency and safety. However, increased automation allows for increased task complexity, which can lead to high cognitive workload and degradation of situational awareness. Adaptive automation is one potential solution to resolve these issues, while maintaining the benefits of traditional automation. Adaptive automation occurs dynamically, with the quantity of automated tasks changing in real-time to meet performance or workload goals. While numerous studies evaluate the relative performance of manual and adaptive systems, little attention has focused on the implications of selecting particular invoking or revoking strategies for adaptive automation. Thus, evaluations of adaptive systems tend to focus on the relative performance among multiple systems rather than the relative performance within a system. This study takes an intra-system approach specifically evaluating the relationship between cognitive workload and situational awareness that occurs when selecting a particular invoking-revoking strategy for an adaptive system. The case scenario is a human supervisory control situation that involves a system operator who receives and interprets intelligence outputs from multiple unmanned assets, and then identifies and reports potential threats and changes in the environment. In order to investigate this relationship between workload and situational awareness, discrete event simulation (DES) is used. DES is a standard technique in the analysis iv of systems, and the advantage of using DES to explore this relationship is that it can represent a human-computer system as the state of the system evolves over time. Furthermore, and most importantly, a well-designed DES model can represent the human operators, the tasks to be performed, and the cognitive demands placed on the operators. In addition to evaluating the cognitive workload to situational awareness tradeoff, this research demonstrates that DES can quite effectively model and predict human cognitive workload, specifically for system evaluation. This research finds that the predicted workload of the DES models highly correlates with well-established subjective measures and is more predictive of cognitive workload than numerous physiological measures. This research then uses the validated DES models to explore and predict the cognitive workload impacts of adaptive automation through various invoking and revoking strategies. The study provides insights into the workload-situational awareness tradeoffs that occur when selecting particular invoking and revoking strategies. First, in order to establish an appropriate target workload range, it is necessary to account for both performance goals and the portion of the workload-performance curve for the task in question. Second, establishing an invoking threshold may require a tradeoff between workload and situational awareness, which is influenced by the task’s location on the workload-situational awareness continuum. Finally, this study finds that revoking strategies differ in their ability to achieve workload and situational awareness goals. For the case scenario examined, revoking strategies based on duration are best suited to improve workload, while revoking strategies based on revoking thresholds are better for maintaining situational awareness.

A spacewalk, or extravehicular activity (EVA), is one of the most mission critical and physically and cognitively challenging tasks that crewmembers complete. With next-generation missions to the Moon and Mars, exploration EVA will challenge crewmembers in partial gravity environments with increased frequency, duration, and autonomy of operations. Given the distance from Earth, associated communication delays, and durations of exploration missions, there is a monumental shift in responsibility and authority taking place in spaceflight; moving from Earth-dependent to crew self-reliant. For the safety, efficacy, and efficiency of future surface EVAs, there is a need to better understand crew health and performance. With this knowledge, technology and operations can be designed to better support future crew autonomy. The focus of this dissertation is to develop and evaluate a psychophysiological monitoring tool to classify cognitive workload during an operationally relevant EVA task. This was completed by compiling a sensor suite of commercial wearable devices to record physiological signals in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. The approach employs supervised machine learning to recognize patterns in psychophysiological features across different psychological states. This relies on the ability to simulate, or induce, cognitive workload in order to label data for training the model. A virtual reality (VR) Translation Task was developed to control and quantify cognitive demands during an immersive, ambulatory EVA scenario. Participants walked on a passive treadmill while wearing a VR headset to move along a virtual lunar surface. They walked with constraints on time and resources, while simultaneously identifying and recalling waypoints in the scene. Psychophysiological features were extracted and labeled according to the task demands, i.e. high or low cognitive workload, for the novel Translation Task, as well as for the benchmark Multi-Attribute Task Battery (MATB). Predictive models were created using the K Nearest Neighbor (KNN) algorithm. The contributions of this dissertation span the simulation, characterization, and modeling of cognitive state. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation. / Doctor of Philosophy / A spacewalk is one of the most important and physically and mentally challenging tasks that astronauts complete. With next-generation missions to the Moon and Mars, exploration spacewalks will challenge astronauts in reduced-weight environments (1/6 and 1/3 Earth's gravity) with longer, more frequent spacewalks and with less help from mission control. To keep astronauts safe while exploring there is a need to better understand astronaut health and performance (physical and mental) during spacewalks. With knowledge of how astronauts will respond to high workload and stressful events, we can plan missions and design tools that can best assist them during spacewalks on the Moon and Mars when help from Earth mission control is limited. Traditional tools of quantifying mental state are not suitable for real-time assessment during spacewalks. Current methods, including subjective surveys and performance-based computer tests, require time and attention to complete and cannot assess real-time operations. The focus of this dissertation is to create a psychophysiological monitoring tool to measure mental workload during a virtual reality (VR) spacewalk. Psychophysiological monitoring uses physiological measures, like heart rate and breathing rate, to predict psychological state, like high workload or stress. Physiological signals were recorded using commercial wearable devices in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. With machine learning, computer models can be trained to recognize patterns in physiological measures for different psychological states. Once a model is trained, it can be tested on new data to predict mental workload. To train and test the models, participants in the studies completed high and low workload versions of the VR task. The VR task was specifically designed for this study to simulate and measure performance during a mentally-challenging spacewalk scenario. The participants walked at their own pace on a treadmill while wearing a VR headset to move along a virtual lunar surface, while balancing their time and resources. They were also responsible for identifying and recalling flags along their virtual path. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.

<p dir="ltr">Task performance is considered an important emphasis in the world of Human-Computer Interaction (HCI). With the emergence of advanced technologies such as Virtual Reality (VR), it is important to understand how individuals are able to utilize this tool for productive task performance. Researchers are continuously exploring how to enhance human performance in a digital space (Wang & Jung, 2011). Prior research has demonstrated the role of integrating haptic feedback into a visual interface, with potential benefits in task performance, as well as increased experiences of presence and awareness while completing HCI tasks. Several research studies have been carried out to investigate ways to optimize human performance and richly uncover factors that affect human performance negatively and positively (Asan et al., 2015). Typically, in a VR setting, three primary senses are engaged: visual, auditory, and tactile (haptic). However, there is a gap in the literature regarding how the availability and intensity of haptic feedback through VR controllers affect users during task performance. This research study seeks to understand the cognitive performance of users in VR when exposed to varying levels of haptic feedback via the VR controllers. Results from this research reveal that participants perceived their performance to be higher and frustration to be lower when they were exposed to moderate and consistent availability and intensity of haptic feedback. To enhance VR’s immersion for users, it is essential to comprehend how to engage the human senses to optimize cognitive performance. Overall, the impact of this research study is to add to an existing body of literature in the domain of haptic feedback for extended reality-based experiences. </p>

Driving a motor vehicle is a complex activity, and errors in performing the driving task can result in crashes which cause property damage, injuries, and sometimes death. It is important that the road environment supports drivers in safe performance of the driving task. At present, increasing amounts of visual information from sources such as roadside advertising create visual clutter in the road environment. There has been little research on the effect of this visual clutter on driving performance, particularly for vulnerable groups such as novice and older drivers. The present work aims to fill this gap. Literature from a variety of relevant disciplines was surveyed and integrated, and a model of the mechanisms by which visual clutter could affect performance of the driving task was developed. To determine potential sources of clutter, focus groups with drivers were held and two studies involving subjective ratings of visual clutter in photographs and video clips of road environments were carried out. This resulted in a taxonomy of visual clutter in the road environment: ‘situational clutter’, including vehicles and other road users with whom drivers interact; ‘designed clutter’, including road signs, signals, and markings used by traffic authorities to communicate with users; and ‘built clutter’, including roadside development and any signage not originating from a road authority. The taxonomy of visual clutter was tested using the change detection paradigm. Drivers were slower to detect changes in photographs of road scenes with high levels of visual clutter than with low levels, and slower for road scenes including advertising billboards than road scenes without billboards. Finally, the effects of billboard presence and lead vehicles on vehicle control, eye movements and responses to traffic signs and signals were tested using a driving simulator. The number of vehicles included appeared to be insufficient to create situational clutter. However billboards had significant effects on driver speed (slower), ability to follow directions on road signs (slower with more errors), and eye movements (increased amount of time fixating on roadsides at the expense of scanning the road ahead). Older drivers were particularly affected by visual clutter in both the change detection and simulated driving tasks. Results are discussed in terms of implications for future research and for road safety practitioners. Visual clutter can affect driver workload as well as purely visual aspects of the driving task (such as hazard perception and search for road signs). When driver workload is increased past a certain point other driving tasks will also be performed less well (such as speed maintenance). Advertising billboards in particular cause visual distraction, and should be considered at a similar level of potential danger as visual distraction from in-vehicle devices. The consequences of roadside visual clutter are more severe for the growing demographic of older drivers. Currently, road environments do not support drivers (particularly older drivers) as well as they could. Based on the results, guidance is given for road authorities to improve this status when designing and location road signage and approving roadside advertising.

This research is important because it has identified a gap in the existing knowledge base. A term is therefore coined to label a computer-based test mode effect, the so-called Item Randomisation Effect, discussed in detail in this thesis. Item Randomisation Effect is a test mode effect occurring in computer-based testing contexts, especially noticeable in test-takers that may be susceptible to test anxiety. The practise of randomising multiple choice items in computer-based test venues is commonplace, mainly as a deterrent for cheating. Previous research attempted to determine the degree of equivalence across testing modalities of any test. The need was to ensure test-takers in paper-based tests would not have an advantage/disadvantage over test-takers given the same test in a computer-based mode. Such studies have a nomothetic perspective. This research contrasts with those earlier studies in that it has an ideographic perspective because it is concerned with the performance of individuals taking any test in the computer-based modality. This subtle difference in perspective may account for the apparent gap in the existing educational research literature. Evidence of Item Randomisation Effect was found in this study but further research into this test mode effect is necessary. / Dissertation (MEd (Computer-Integrated Education))--University of Pretoria, 2008. / Curriculum Studies / unrestricted