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Total results: 588

A QE Study on Golf Putting

Year: 2022

Authors: S Song, DW Han

[목적] QE는 주요 움직임 직전에 나타나는 시선고정시간이다. QE는 숙련자가 운동기술을 사용할 때 나타나는 특징을 가지고 있다. 본 연구의 목적은 골프 퍼팅에서의 QE 원인을 규명하고 초보자에게 퍼팅 수행 향상을 위한 연습 방법을 제시하는 것이다. [방법] 연구대상자(n=30)는 통제, QE, 시각차단 집단으로 각각 10명씩 무선 배정하였고 집단 별 연습 방법에 따라 2 일간 골프 퍼팅 연습을 하였다. 24시간 후 골프 퍼팅에 대한 파지검사와 연구대상자간의 경쟁 시합을 하였고 집단과 기간에 따라 퍼팅 점수, QE, 스윙시간, 후두엽의 알파파워를 산출하여 비교 분석하였다. [결과] 첫째, 세 집단 모두 사전보다 파지, 경쟁에서 퍼팅 점수가 높아진 것으로 나타났다. 특히 QE집단은 통제집단보다 높은 퍼팅 점수를 보이며 통계적으로 유의한 차이가 나타났다. 그러나 시각차단 집단은 두 집단과의 차이가 나타나지 않았다. 둘째, QE집단은 사전보다 파지, 경쟁에서 QE가 통계적으로 길어지는 것으로 나타났다. 그리고 통제집단은 사전보다 경쟁에서 QE가 다소 길어지는 경향을 보였다. 그러나 시각차단 집단은 기간에 따라 QE의 변화가 나타나지 않았다. 셋째, 세 집단 모두 기간이 지남에 따라 스윙시간이 유의하게 길어졌다. 넷째, 후두엽의 알파파워는 집단과 기간에 따라 유의한 차이가 나타나지 않았다. [결론] 시각차단 집단은 자세가 안정적이게 변함에 불구하고 QE는 길어지지 않았으나, 눈을 뜨고 연습한 QE집단은 QE가 길어졌고 통제집단도 QE가 길어지는 경향을 보였다. 그러므로 QE는 자세 조절 보다 시각에 의한 인지적 처리로 해석 할 수 있다. 마지막으로 본 연구를 통해 골프 퍼팅 초보자에게 QE연습이 보다 효율적인 연습방법임을 증명하였다.

1 version available:

A study of the challenges of eye tracking systems and gaze interaction for individuals with motor disabilities

Year: 2022

Authors: L Huang, C Xu,T Westin,J Dupire, F Le Lièvre

Eye tracking systems are crucial methods by which motor disabled people can interact with computers. Previous research in this field has identified various accessibility affecting eye tracking technologies and applications. However, there is limited research into first-hand user experiences among individuals with motor disabilities. This study aims to examine the actual challenges with eye tracking systems and the gaze interaction faced by motor disabled people. A survey was conducted among people with motor disabilities who used eye trackers for computer interactions. It reveals the current issues from their first-hand experiences in three areas: eye tracking program, gaze interaction, and accessible applications. A knowledge graph arising from the survey delineates the connections among the eye tracking usability issues. The survey’s results also indicate practical strategies for future improvements in eye trackers.

5 versions available

Adaptive Driving Assistant Model (ADAM) for advising drivers of autonomous vehicles

Year: 2022

Authors: SJ Hsieh, AR Wang,A Madison,C Tossell

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.

2 versions available

Age Differences in the Situation Awareness and Takeover Performance in a Semi-Autonomous Vehicle Simulator

Year: 2022

Authors: Y Murzello

Research on young and elderly drivers indicates a high crash risk amongst these drivers in comparison to other age groups of drivers. Young drivers have a greater propensity to adopt a risky driving style and behaviors associated with poor road safety. On the other hand, age-related declines can negatively impact the performance of older drivers on the road leading to crashes and risky maneuvers. Thus, autonomous vehicles have been suggested to improve the road safety and mobility of younger and older drivers. However, the difficulty of manually taking over control from semi-autonomous vehicles might vary in different driving conditions, particularly in those that are more challenging. Hence, the present study aims to examine the effect of road geometry and scenario, by investigating young, middle-aged and older drivers’ situation awareness (SA) and takeover performance when driving a semi-autonomous vehicle simulator on a straight versus a curved road on a highway and an urban non-highway road when engaged in a secondary distracting task. Due to the impact of COVID-19, data from only the young (n=24) and middle-aged (n=24) adults were collected and analyzed. Participants drove a Level 3 semi-autonomous simulator vehicle and performed a secondary non-driving related task in the distracted conditions. The results indicated that the participants had significantly longer hazard perception times on the curved roads and autopilot drives, but there was no significant effect of driver age and road type. Their Situation Awareness Global Assessment Technique (SAGAT) scores were higher in the highway scenarios, on the straight roads, and in the manual drive compared to the autopilot with distraction drive. Young drivers were also found to have significantly higher SAGAT scores than middle-aged drivers. While there was a significant interaction effect between road type and road geometry on takeover time, there was no significant main effect of road geometry, drive type and driver’s age. For the takeover quality metrics, road geometry and drive type had an effect on takeover performance. The resulting acceleration was higher for the straight road and in the autopilot drives, and the lane deviation was higher on the curved road and autopilot only drive compared to the autopilot with distraction drive. There was no significant main effect of road type and driver’s age on resulting acceleration and lane deviation. Overall, while there were age differences in some aspects of SA, young and middle-aged drivers did not differ in their takeover performance. The participants’ SA was impacted by the road type and geometry and their takeover quality varied according to the road geometry and drive type. The outcomes of this research will aid vehicle manufacturing companies that are developing Level 3 semi-autonomous vehicles with appropriately designing the lead time of the takeover request to meet the driving style and abilities of younger and middle-aged drivers. This will also help to improve road safety by reducing the crash rate of younger drivers.

1 version available:

Analyzing Korean Reading Course and Designing Education-A Focus on Learner’s Eye-Tracking Analysis

Year: 2022

Authors: SN Min, J Im,M Subramaniyam

The purpose of this study is to propose a model for designing reading education that can improve reading ability by finding and linguistically analyzing how the reading ability required for reading comprehension is presented at each learner level. In this study, the eye-tracking experiment was conducted divided into lexical and sentence levels which are the stage of comprehension corresponding to the sub-element of the reading comprehension, and the results are as follows. First, at the lexical level, it was confirmed that cognitive speed differs depending on the degree of preservation of the base form of the conjugations of the predicates with the same base form. Second, at the sentence level, we confirmed that the unstable reading-comprehension process appeared in the complex compared to the short sentence, even if the sentence length, vocabulary level, and vocabulary were similar and in the case of that the sentence has the same meaning. This paper suggests that the spelling, vocabulary, conjugation, grammar knowledge that correspond to the lower level should be included as the elements of education.

1 version available:

Analyzing the influencing factors and workload variation of takeover behavior in semi-autonomous vehicles

Year: 2022

Authors: H Zhang, Y Zhang, Y Xiao, C Wu

There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investigated from subjective and objective viewpoints. Thirty-seven drivers were recruited to participant the semi-autonomous driving experiments, and the drivers were required to complete different NDRTs (Non-Driving-Related Tasks): mistake finding, chatting, texting, and monitoring when the vehicle is in autonomous mode. Then, we introduced collision warning to signal there is risk ahead, and the warning signal was triggered at different TB (Time Budget)s before the risk, at which time the driver had to take over the driving task. During driving, the NASA-TLX-scale data were obtained to analyze the variation of the driver’s subjective workload. The driver’s pupil-diameter data acquired by the eye tracker from 100 s before the TOR (Take-Over Request) to 19 s after the takeover were obtained as well. The sliding time window was set to process the pupil-diameter data, and the 119-s normalized average pupil-diameter data under different NDRTs were fitted and modeled to analyze the variation of the driver’s objective workload. The results show that the total subjective workload score under the influence of different factors is as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s and TB = 3 s have no significant difference; and mistake finding > chatting > texting > monitoring. The results of pupil-diameter data under different factors are as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s > TB = 3 s; and monitoring type (chatting and monitoring) > texting type (mistake finding and texting). The research results can provide a reference for takeover safety prediction modeling based on workload.

13 versions available

Assessing the impact of driver advisory systems on train driver workload, attention allocation and safety performance

Year: 2022

Authors: VJMP Verstappen, EN Pikaar, RGD Zon

Netherlands Railways has developed driver advisory systems (DAS) to provide the train driver with route context information and coasting advice in order to benefit punctuality and energy efficiency. However, the impact of these DAS on human factors aspects and safety performance is unclear. The current study assesses the impact of two DAS levels (route context information and coasting advice) on mental workload, attention allocation and safety performance, using eye tracking, a subjective mental workload rating scale (RSME) and simulator data. The overall findings suggest that the application of DAS levels has no negative impact on safety performance and attention allocation towards the trackside compared to a control condition with static timetable information. Furthermore, safety performance benefits significantly from DAS with route context information. DAS were originally developed to benefit punctuality and energy efficiency goals. This study implicates that DAS can also benefit safety performance. The current study found that DAS could decrease workload when the functionalities meet the requirements of the situation. The possible presence of mental underload and its effect on driving performance should be taken into consideration when implementing DAS. It is essential in the development of DAS that it meaningfully enriches the train driving task in stead of simply increasing mental workload.

7 versions available

Classification of driver cognitive load based on physiological data: Exploring recurrent neural networks

Year: 2022

Authors: S Kumar,D He, G Qiao

In-vehicle systems can lead to high cognitive load that impairs driving performance. Interfaces that can detect and adapt to cognitive load accordingly may alleviate these effects. Previous research explored machine learning models to classify drivers’ cognitive load based on physiological signals but most conducted training and testing on data from the same participants (i.e., within-driver partitioning), which raises generalizability and practical feasibility concerns. In this paper, we explored the performance of widely-used models by training and testing them on data from different subjects (i.e., across-drivers partitioning), and further compared them with a more recent model that is effective for time-series data, the recurrent neural network (RNN). A driving simulator dataset was used to classify 2 levels of cognitive load (external cognitive secondary task vs. no task). All models performed better with within-driver partitioning. RNN outperformed other models with mean accuracies of 88.1% and 85.6% with within-driver and across-drivers partitioning, respectively.

4 versions available

Classification of driver cognitive load: Exploring the benefits of fusing eye-tracking and physiological measures

Year: 2022

Authors: D He, Z Wang,EB Khalil,B Donmez

In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

7 versions available

Classification of flight phases based on pilots’ visual scanning strategies

Year: 2022

Authors: V Peysakhovich,W Ledegang,M Houben

Eye movements analysis has great potential for understanding operator behaviour in many safety-critical domains, including aviation. In addition to traditional eye-tracking measures on pilots’ visual behavior, it seems promising to incorporate machine learning approaches to classify pilots’ visual scanning patterns. However, given the multitude of pattern measures, it is unclear which are better suited as predictors. In this study we analyzed the visual behaviour of eight pilots, flying different flight phases in a moving-base flight simulator. With this limited dataset we present a methodological approach to train linear Support Vector Machine models, using different combinations of the attention ratio and scanning pattern features. The results show that the overall accuracy to classify the pilots’ visual behaviour in different flight phases, improves from 51.6% up to 64.1% when combining the attention ratio and instrument scanning sequence in the classification model.

5 versions available