Exploring Eye Tracking to Detect Cognitive Load in Complex Virtual Reality Training

• URL: http://arxiv.org/abs/2411.12771v1
• Date: Mon, 18 Nov 2024 16:44:19 GMT
• Title: Exploring Eye Tracking to Detect Cognitive Load in Complex Virtual Reality Training
• Authors: Mahsa Nasri, Mehmet Kosa, Leanne Chukoskie, Mohsen Moghaddam, Casper Harteveld,
• Abstract summary: We present an ongoing study to detect users' cognitive load using an eye-tracking-based machine learning approach. We developed a VR training system for cold spray and tested it with 22 participants. Preliminary analysis demonstrates the feasibility of using eye-tracking to detect cognitive load in complex VR experiences.
• Score: 11.83314968015781
• License:
• Abstract: Virtual Reality (VR) has been a beneficial training tool in fields such as advanced manufacturing. However, users may experience a high cognitive load due to various factors, such as the use of VR hardware or tasks within the VR environment. Studies have shown that eye-tracking has the potential to detect cognitive load, but in the context of VR and complex spatiotemporal tasks (e.g., assembly and disassembly), it remains relatively unexplored. Here, we present an ongoing study to detect users' cognitive load using an eye-tracking-based machine learning approach. We developed a VR training system for cold spray and tested it with 22 participants, obtaining 19 valid eye-tracking datasets and NASA-TLX scores. We applied Multi-Layer Perceptron (MLP) and Random Forest (RF) models to compare the accuracy of predicting cognitive load (i.e., NASA-TLX) using pupil dilation and fixation duration. Our preliminary analysis demonstrates the feasibility of using eye tracking to detect cognitive load in complex spatiotemporal VR experiences and motivates further exploration.

Related papers

• Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR [11.021668923244803]
  Relationship between cognitive activities, physical activities, and familiar feelings of cybersickness is not well understood. We collected head orientation, head position, eye tracking, images, physiological readings from external sensors, and self-reported cybersickness severity, physical load, and mental load in VR.
  arXiv  Detail & Related papers  (2024-09-10T22:41:14Z)
• Tremor Reduction for Accessible Ray Based Interaction in VR Applications [0.0]
  Many traditional 2D interface interaction methods have been directly converted to work in a VR space with little alteration to the input mechanism. In this paper we propose the use of a low pass filter, to normalize user input noise, alleviating fine motor requirements during ray-based interaction.
  arXiv  Detail & Related papers  (2024-05-12T17:07:16Z)
• Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning [0.0]
  This research contributes significantly to the Thelxino"e framework, aiming to enhance VR experiences by integrating multiple sensor data for realistic and emotionally resonant touch interactions. Our findings open new avenues for developing more immersive and interactive VR environments, paving the way for future advancements in virtual touch technology.
  arXiv  Detail & Related papers  (2024-03-27T21:14:17Z)
• Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation [57.60490773016364]
  We combine vision and touch sensing on a multi-fingered hand to estimate an object's pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation.
  arXiv  Detail & Related papers  (2023-12-20T22:36:37Z)
• 3D Gaze Vis: Sharing Eye Tracking Data Visualization for Collaborative Work in VR Environment [3.3130410344903325]
  We designed three different eye tracking data visualizations: gaze cursor, gaze spotlight and gaze trajectory in VR scene for a course of human heart. We found that gaze cursor from doctors could help students learn complex 3D heart models more effectively. It indicated that sharing eye tracking data visualization could improve the quality and efficiency of collaborative work in the VR environment.
  arXiv  Detail & Related papers  (2023-03-19T12:00:53Z)
• Virtual-Reality based Vestibular Ocular Motor Screening for Concussion Detection using Machine-Learning [0.0]
  Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. Current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. With the advancement of technology, virtual reality (VR) can be utilized to advance the standardization of the VOMS.
  arXiv  Detail & Related papers  (2022-10-13T02:09:21Z)
• Force-Aware Interface via Electromyography for Natural VR/AR Interaction [69.1332992637271]
  We design a learning-based neural interface for natural and intuitive force inputs in VR/AR. We show that our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. We envision our findings to push forward research towards more realistic physicality in future VR/AR.
  arXiv  Detail & Related papers  (2022-10-03T20:51:25Z)
• Learning Effect of Lay People in Gesture-Based Locomotion in Virtual Reality [81.5101473684021]
  Some of the most promising methods are gesture-based and do not require additional handheld hardware. Recent work focused mostly on user preference and performance of the different locomotion techniques. This work is investigated whether and how quickly users can adapt to a hand gesture-based locomotion system in VR.
  arXiv  Detail & Related papers  (2022-06-16T10:44:16Z)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)
• Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments [66.83839051693695]
  Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. We propose to leverage recent advances in 3D virtual environments in order to approach the automatic generation of potentially life-long dynamic scenes with photo-realistic appearance. A novel element of this paper is that scenes are described in a parametric way, thus allowing the user to fully control the visual complexity of the input stream the agent perceives.
  arXiv  Detail & Related papers  (2021-09-16T10:37:21Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.