Eye-Tracking for Performance Evaluation and Workload Estimation in Space Telerobotic Training

Guo, Yao; Freer, Daniel; Deligianni, Fani; Yang, Guang‐Zhong

doi:10.1109/thms.2021.3107519

Cited by 26 publications

(15 citation statements)

References 38 publications

(75 reference statements)

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“…The highest accuracy obtained was 99.3 (Lagodzinski et al, 2018) and 99.21% (Yoo et al, 2019), with the features of EOG and gaze position, respectively. The study of Guo et al (2021) has the lowest accuracy among the studies found, which achieved an accuracy of 49.32%. Based on Table 2, the least successful approaches achieved 71.09% using pupil size and gaze position with the combination of EEG signals.…”

Section: Discussionmentioning

confidence: 89%

“…The RFFT assesses the ability of an individual to generate new figures using five different dot configurations. In a study by Guo et al (2021), the authors conducted an experiment for workload estimation and explore the usage of eye-tracking technology in performance evaluation in space teleoperation with two confounding factors, which are latency and time pressure. The eye features used in this investigation included eye blink, fixation, saccade, gaze, and pupil diameter.…”

Section: Blinkmentioning

confidence: 99%

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Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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ContextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.MethodsWe performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.ResultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.ConclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.

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Section: Discussionmentioning

confidence: 89%

Section: Blinkmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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“…In contrast to the method based on MI and EOG presented by He et al [ 41 ], our asynchronous system does not require training and is easier to operate. In addition, fixation duration, saccade frequency, and duration affect subjects' mental load and reduce task efficiency [ 42 ]. Next, we will improve attention based on finding a reasonable fixation duration and task duration.…”

Section: Discussionmentioning

confidence: 99%

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals

Cai

Pan

2022

Journal of Healthcare Engineering

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This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.

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“…Data transfer between human and the remote robot is realised via TCP/IP protocol, where the control frequency and additional latency are 100Hz and 0.5s. Inspired by [45], latency is introduced to test the savings in communication resources and robustness against delay-induced perturbations. The control objective for the remote robot is to track the state signal sent from the human operator.…”

Section: B Experiments On Heterogeneous Teleoperation Systemmentioning

confidence: 99%

Adaptive Event-Triggered Control for Nonlinear Systems With Asymmetric State Constraints: A Prescribed-Time Approach

Wang

Lam

Guo

et al. 2023

IEEE Trans. Automat. Contr.

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Finite/Fixed-time control yields a promising tool to optimize a system's settling time, but lacks the ability to separately define the settling time and the convergence domain (known as practically prescribed-time stability, PPTS). We provide a sufficient condition for PPTS based on a new piecewise exponential function, which decouples the settling time and convergence domain into separately user-defined parameters. We propose an adaptive event-triggered prescribed-time control scheme for nonlinear systems with asymmetric output constraints, using an exponentialtype barrier Lyapunov function. We show that this PPTS control scheme can guarantee tracking error convergence performance, while restricting the output state according to the prescribed asymmetric constraints. Compared with traditional finite/fixed-time control, the proposed methodology yields separately user-defined settling time and convergence domain without the prior information on disturbance. Moreover, asymmetric state constraints can be handled in the control structure through bias state transformation, which offers an intuitive analysis technique for general constraint issues. Simulation and experiment results on a heterogeneous teleoperation system demonstrate the merits of the proposed control scheme.

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Eye-Tracking for Performance Evaluation and Workload Estimation in Space Telerobotic Training

Cited by 26 publications

References 38 publications

Eye-Tracking Feature Extraction for Biometric Machine Learning

Eye-Tracking Feature Extraction for Biometric Machine Learning

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals

Adaptive Event-Triggered Control for Nonlinear Systems With Asymmetric State Constraints: A Prescribed-Time Approach

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