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Supplementary data for the paper: Situation awareness based on eye movements in relation to the task environment.
No full textSupplementary data for the paper: de Winter, J.C.F., Eisma, Y.B., Cabrall, C.D.D., Hancock, P.A., and Stanton, N.A.; Situation awareness based on eye movements in relation to the task environment; Cognition, Technology and Work
Visual Attention in Human−Machine Interaction
No full textHumans are incapable of attending to everything at the same time. The serial nature of focused attention limits the information intake capacity of the perceptual system. This thesis deals with the measurement and modelling of visual attention distribution. It is examined whether measures of visual attention are predictive of task performance. The first chapter introduces the main topic of this thesis: the complex nature of modern technological systems, which feature many information sources that have to be monitored. Many psychological constructs have been proposed in the human factors literature that have alleged criterion validity for task performance. Here, task performance is regarded as the human’s ability to e.g., take over control of an automated system in potential critical situations. Contrary to the speculative nature of some of the Human- Factors constructs, this thesis sets out to capture performance in terms of objective measures of visual attention. Wickens’s (2008) Salience, Effort, Expectancy, Value (SEEV) model is introduced and discussed. This model is utilized for interpreting the eye-tracking results. Finally, a rationale for the topics in the thesis is provided. Chapters 2 through 4 of this thesis discuss and elaborate on Senders’s (1983) research in detail, by means of replication research and an extensive tutorial on his mathematical models. These chapters provide an empirical underpinning and conceptual understanding of the concept of visual attention. Chapters 5 through 8 discuss visual attention in light of Air Traffic Control (ATC) and automated driving, and are regarded as suitable cases for attention distribution measurement and task performance prediction. Chapter 9 investigates task performance and visual attention in a psychometric task: Inspection Time, which provides a good testbed for operationalizing the effect of attention on task performance. Chapter 10 concludes with a discussion on the topics in this thesis...Human-Robot Interactio
Sensorimotor Rhythm as Control Signal in EEG-Based Brain-Computer Interfaces
No full textBackground: A brain-computer interface (BCI) is a system that enables humans to control a computer by their brain signals. This can be achieved by modulating the sensorimotor rhythm (SMR) through both motor execution and motor imagery. The potential enhancement of spinal reflexes and motor control through SMR training is attributed to the hypothesis that activity-dependent brain plasticity guides spinal plasticity during motor skill learning. However, the signal processing needed for conversion from raw brain signals to a robust control signal is challenging. The recorded electroencephalogram (EEG) signals are contaminated by multiple unknown sources and suffer from inter-subject variability, complicating the development of the BCI. Objective: To obtain a robust control signal the study 1) investigated the relation between event-related desynchronization (ERD) and mechanical stretch reflex size in the flexor carpi radialis across four muscle pre-loads consisting of 0%, 5%, 25% and 40% of maximum voluntary contraction (MVC), 2) investigated the ability of three offline signal processing paradigms in distinguishing between periods of rest and activity using EEG data associated with motor execution and motor imagery, 3) built a pseudo-online signal processing paradigm to simulate real-time signal processing based on a single trial and a continuous data stream. Method: Mechanical stretch perturbations were applied to the wrist under four percentages of MVC during motor execution and imagery conditions in six healthy subjects. The data anal- ysis encompassed signal processing techniques including pre- processing with a large Laplacian filter, feature extraction through autoregressive modelling (AR), power spectral density (PSD), or discrete wavelet transform (DWT), and classification using linear discriminant analysis (LDA). Results: Mechanical stretch reflex sizes and ERD amplitude significantly increased with increasing percentage of MVC for motor execution trials. For motor imagery trials, no significant correlation was found between the stretch reflex size and ERD amplitude. The offline signal processing paradigms resulted in classification accuracies of 73.55% (PSD), 71.96% (DWT) and 57.13% (AR). The classification accuracies significantly increased with increasing percentage of MVC. The pseudo-online paradigm resulted in a mean classification accuracy of 51.38%. Conclusions: The EEG-based BCI shows potential for enhancing the functional recovery of patients with motor disorders. The findings demonstrate that feature extraction methods PSD and DWT could effectively distinguish between periods of rest and activity in motor execution data. Nevertheless, for the intended application, including real-time processing based on single trial motor imagery data, BCI performance should be improved. Future research should focus on motor imagery EEG data encompassing motor imagery training and feedback on motor imagery performance. Mechanical Engineerin
External Human-Machine Interfaces on Autonomous Vehicles: The effect of message perspective and memory load on pedestrian crossing intentions
No full textPerspective-taking is the ability to see a situation based on the viewpoint of others. In autonomous vehicle-pedestrian (AV-P) interaction, the perspective taken by the pedestrian could be affected by the design of an external Human-Machine Interface (eHMI). However, currently, there is little knowledge about the effect of message perspective on the crossing intentions of pedestrians when interpreting the intention of an AV. This study aims to investigate the effect of eHMI message perspective and cognitive load on participants’ perspective-taking, as inferred from their crossing intentions. We designed a photo-based experiment and examined the effect of message perspective (egocentric (from the pedestrians' point of view): ‘WALK’, ‘DON’T WALK’ vs. allocentric: ‘BRAKING’, ‘DRIVING’ vs. ambiguous ‘GO’, ‘STOP’), and cognitive load on the crossing intentions, response times and pupil diameter of the participans (N = 103). We added a memory task to increase the cognitive load during two-thirds of the trials, since crossing intentions can be demanding (the traffic scenario can be complex complex or the pedestrian is distracted) and therefore might influence perspective-taking. The results showed that the egocentric messages were most persuasive as demonstrated by more uniform crossing intentions and faster response times compared to allocentric and ambiguous messages. When participants were put under cognitive load, a more efficient strategy was used to make a crossing decision as demonstrated by faster yet consistent crossing intentions compared to no memory task. No difference in cognitive load was measured for both message perspective and cognitive load at the moment of response, as evidenced by equal pupil size. Concerning the ambiguous messages, ‘GO’ encouraged crossing and the ‘STOP’ inhibited crossing, which points towards an egocentric perspective taken by the pedestrian. We conclude that pedestrians initially take an egocentric perspective if the eHMI message is ambiguous, though this egocentric bias can be overcome by using explicitly an egocentric or allocentric eHMI message perspective. In addition, we conclude that participants perform better (more uniform crossing decisions, faster responses) when the eHMI’s message perspective is egocentric rather than allocentric. Biomedical Engineerin
Gaze-contingent interfaces: The effect on noticing times of time critical warning messages under Level 2 driving conditions while interacting with in-vehicle technology
No full textWith the introduction of automated driving systems come benefits such as the improvement of traffic safety. However, with an increasing level of automation in vehicles also comes an increase in interaction with in-vehicle technology by drivers while they are meant to supervise the automated driving systems. Due to more interaction with in-vehicle technology and a vigilance decrement of the driver in Level 2 driving, an increase in reaction time of the driver is seen when intervention is needed by the means of a take over request. This delay in reaction by the driver opposes the benefit of the introduction of automated driving systems and causes hazardous situations. To try and circumvent the effect of vigilance decrement, this paper attempts to demonstrate the reduction of noticing time of the Hands-On-Wheel warning message for drivers of Level 2 vehicles while interacting with in-vehicle technology through the implementation of a gaze-contingent interface. The results of this experiment indicate a 79.3% lower noticing time of the Hands-On-Wheel warning message when the stimulus is placed in a gazecontingent manner, while the participants engage in secondary tasks on the in-vehicle technology. The placement of the stimulus on the head unit when the participant is already looking at it reduces the primary task load of touching the steering wheel and causes for the stimulus to be seen quicker as compared to a static interface. However, the performance of the secondary task seems to decrease when using a gaze-contingent interface. This is due to the intrusive nature of the placement of the stimulus, which demands the driver to store information regarding the secondary task in their working memory while they attend to the primary task. Despite the decline in secondary task performance, the reduction of noticing times of time critical messages when placed in a gaze-contingent manner could be beneficial to the safety of autonomous driving functions where the driver has a vigilance task and is engaging in secondary tasks.Mechanical Engineering | Vehicle Engineering | Human Factor
Visual sampling processes revisited: replicating and extending senders (1983) using modern eye-tracking equipment
No full textIn pioneering work, Senders (1983) tasked five participants to watch a bank of six dials, and found that glance rates and times glanced at dials increase linearly as a function of the frequency bandwidth of the dial's pointer. Senders did not record the angle of the pointers synchronously with eye movements, and so could not assess participants’ visual sampling behavior in regard to the pointer state. Because the study of Senders has been influential but never repeated, we replicated and extended it by assessing the relationship between visual sampling and pointer state, using modern eye-tracking equipment. Eye tracking was performed with 86 participants who watched seven 90-second videos, each video showing six dials with moving pointers. Participants had to press the spacebar when any of the six pointers crossed a threshold. Our results showed a close resemblance to Senders’ original results. Additionally, we found that participants did not behave in accordance with a periodic sampling model, but rather were conditional samplers, in that the probability of looking at a dial was contingent on pointer angle and velocity. Finally, we found that participants sampled more in agreement with Nyquist sampling when the high bandwidth dials were placed in the middle of the bank rather than at its outer edges. We observed results consistent with the saliency, effort, expectancy, and value model and conclude that human sampling of multidegree of freedom systems should not only be modeled in terms of bandwidth but also in terms of saliency and effort.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & SimulationIntelligent VehiclesBiomechatronics & Human-Machine Contro
Sampling behavior while detecting conflicts between linear moving stimuli
No full textIntroduction. Air traffic controllers (ATCo’s) are responsible for a safe and efficient air traffic flow, and therefore, they are required to be excellent in conflict detection. Various studies have uncovered relationships between conflict geometry (e.g., conflict angle) and operators’ abilities to detect conflicts. However, little is known about the underlying perceptive and cognitive processes during conflict detection tasks. Knowledge of these processes could give insight into how ATCo’s could be supported. In order to discover how people look at typical air traffic control (ATC) situations, a simplified ATC scenario was presented to novice participants in which two dots (representing aircraft) moved towards each other. Methods. The eye movements of 35 participants were recorded during an experiment in which they had to indicate whether a conflict was present or not. Each participant watched 36 different videos with a duration of 20 seconds and different air traffic geometries. The independent variables were: (1) conflict angle (2) configuration (3) closest distance of approach, and (4) discrete vs continuous moving stimuli (2 Hz or 30 Hz). Results. The results show that continuous moving trials obtained a significantly higher performance score, and more and shorter fixations were found compared to discrete moving trials. Furthermore, in accordance with Neal and Kwantes (2009) and Remington et al. (2000), participants performed significantly better with a conflict angle of 30 degrees compared to larger angles. However, it was found that the performance score with a conflict angle of 100 degrees was lower and the self-experienced difficulty was higher, compared to 150 degrees. The eye movement variables showed a monotonic relation with the conflict angle: when the number of fixations increased, the fixation duration decreased with the higher conflict angles. Furthermore, participants sampled more often from one dot to the other and exhibited less pursuit movement with increasing conflict angles. Moreover, it was found that conflict detection was significantly easier when one of the dots moved diagonally. Finally, the results show that for trials in which no conflict occurred, participants exhibited more fixations and sampled more from one dot to the other, compared to trials with a conflict. Conclusions. We conclude that novice participants are better at detecting conflicts with continuous moving stimuli compared to discretely moving stimuli. If further research shows the same increase in performance with continuous motion in real ATC, flight radars could be adjusted accordingly. Also, it is concluded that the conflict angle has influence on eye movements. Indications are found that conflict angles close to 0 and 180 degrees are easier for detecting conflicts. Further research with various conflict angles is recommended. Furthermore, indications are found that diagonal movements might be easier for conflict detection. Moreover, we conclude that in our experiment pursuit movements are preferred with vertical movements compared to horizontal movement. Finally, we conclude that participants sample from one dot to the other when the dots are further away from each other, but when they come closer to each other, pursuit movement is often used to follow both dots at the same time.Mechanical Engineerin
Personalized and Adaptive Cognitive Human-Robot Interaction with a Novel Fuzzy Logic Control and Reinforcement Learning-Based Paradigm: Master Thesis Report
No full textAn aging population puts a pressure on health-care workers working with dementia patients globally. A potential solution is to provide care with Socially Assistive Robots (SARs), i.e. robots who help people through social interaction. However, for effective care these SARs must be able to personalize their behavior to individual patients and adapt this behavior to changes in this patient’s references. This paper presents the decision making process of a SAR that enables the SAR to personalize its behavior to the personality of the patients and adapt this behavior to their current state-of-mind. The system consists of a Fuzzy Logic Control personalization module, which personalizes the SAR’s behavior and a Reinforcement Learning based decision making module together with a Fuzzy Logic Control reward module for the adaption of the personalized behavior. The personalization of the SAR’s behavior is assessed by comparing the output of the system with answers from a survey. The average scatter index over all different behavioral parameters of the SAR is 20.4% The adaptation of the behavior is assessed with computer-based simulations, where an overall accuracy of 81.8% is achieved. A third experiment is carried out to assess the effect of adding the Fuzzy Logic Control personalization module to the system. This experiment shows that adding the personalization module to the decision making system of the SAR decreases the time for the learning process to converge with 13.3%. Although the first assessments of the system look promising, more extensive experiments should be held in later stages of the research. A crucial experiment that must be held in future research is performing real-life interactions between dementia patients and the SAR, in such an experiment the functionality of the system really can be assessed.Aerospace Engineerin
How Do People Perform an Inspection Time Task?: An Examination of Visual Illusions, Task Experience, and Blinking
No full textIn the inspection time (IT) paradigm, participants view two lines of unequal length (called the Pi-figure) for a short exposure time, and then judge which of the two lines was longer. Early research has interpreted IT as a simple index of mental speed, which does not involve motor activity. However, more recent studies have associated IT with higher-level cognitive mechanisms, including focused attention, task experience, and the strategic use of visual illusions. The extent to which these factors affect IT is still a source of debate. We used an eye-tracker to capture participants’ (N = 147) visual attention while performing IT trials. Results showed that blinking was time-dependent, with participants blinking less when the Pi-figure was visible as compared to before and after. Blinking during the presentation of the Pi-figure correlated negatively with response accuracy. Also, participants who reported seeing a brightness illusion had a higher response accuracy than those who did not. The first experiment was repeated with new participants (N = 159), enhanced task instructions, and the inclusion of practice trials. Results showed substantially improved response accuracy compared to the first experiment, and no significant difference in response accuracy between those who did and did not report illusions. IT response accuracy correlated modestly (r = 0.18) with performance on a short Raven’s advanced progressive matrices task. In conclusion, performance at the IT task is affected by task familiarity and involves motor activity in the form of blinking. Visual illusions may be an epiphenomenon of understanding the IT task.Human-Robot Interactio
Influential stimuli characteristics on SNR in SSVEP-based interfaces: Thesis report: researching different aspects that influence the SNR in SSVEP-based interfaces
No full textDifferent paradigms can be used to evoke brainwaves. These brainwaves can be interpreted as commands that can be used to control different applications. These frameworks that interpret the brainwaves are called brain-computer interfaces. Steady-state visually evoked potentials is one of these paradigms that uses external stimuli flickering at fixed frequencies to evoke brainwaves. This paradigm is fast to issue, is reliable, and needs no training time of the user. However, to be able to distinguish multiple commands it is important to distinguish between multiple commands reliably. The fundamental metric that determines the signal quality is the signal-to-noise ratio. The signal-to-noise ratio is measured in decibels and is the ratio in power between the signal that is evoked around the stimulus frequency and the power of some baseline, referred to as noise. To extract the power the discrete Fourier transform is calculated from the measured brainwaves. The brainwaves are measured in millivolts over time. These brainwaves can be recorded using implants in the head named intracortical or external, such as electroencephalography. External methods such as electroencephalography are much safer for the user. Important to recognize is that one of the contributing factors to signal-to-noise ratio are the characteristics of the external stimuli displayed. Research is still trying to figure out the exact relationship between stimuli characteristics and signal-to-noise ratio. However, the experiments of previous research lack the context of the gaze of the subjects to explain the electroencephalography recordings. In this research, this is attempted to be solved using eye tracking. Furthermore, there is a research gap in thethe scientific field surrounding signal-to-noise ratio and stimuli characteristics as the effect of surroundingstimuli on the measured signal-to-noise ratio of the target stimulus has never been investigated.This research attempted to solve these problems by performing 2 experiments with 6 participants. One experiment shows a single stimulus across various shapes (triangles, squares, and circles), colors (red, green, and white), frequencies (9, 13, 19, and 25Hz), and sizes (10.000, 20.000, 30.000 pixels). The other experiment simulates the natural environment of the external stimuli across the same frequencies, colors, and shapes. The natural environment of a single stimulus is actually surrounding stimuli at different frequencies, as applications often require the ability to distinguish between multiple different commands. Thus, to state the main research question: "What is the relationship between the stimuli characteristics and the measured SNR?".This question is answered by dissecting the effect that color, shape, size, frequency, and surrounding stimuli have on the signal-to-noise ratio. Dr.ir. Y.B. Eisma was the supervisor of S.T. van Vliet. Using his equipment the brainwaves were recorded at the UMC Amsterdam. https://github.com/SjoerdTimovanVliet/SSVEP_interface_thesisMechanical Engineering | Vehicle Engineering | Cognitive Robotic
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