Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Zhang, Lian; Wade, Joshua; Bian, Dayi; Fan, Jing; Swanson, Amy; Weitlauf, Amy; Warren, Zachary; Sarkar, Nilanjan

doi:10.1109/taffc.2016.2582490

Cited by 107 publications

(64 citation statements)

References 53 publications

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“…Nevertheless, in the long run, obtaining responses through EEG has the potential to develop brain-computer interfaces to improve social skills of individuals with ASD [39]. EEG data were also collected in [141][142][143][144]. In [145], Ozcan et al reported a design for wearable companion toys in which biosensors can be embedded to measure the participant's physiological and emotional state.…”

Section: Physiological Data Collectionmentioning

confidence: 99%

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Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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Abstract:The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?

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Section: Physiological Data Collectionmentioning

confidence: 99%

“…In addition to eye gaze, we have seen studies that employ various methods to obtain user feedback for adaptive control of a treatment/education program, including facial expression, head orientation, human gesture tracking, and physiological data collection [135,136,141,143,146,151].…”

Section: Automatic Adaption Of Programsmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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“…The β activity corresponds to the frequency band [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz. It is present on both sides of the brain symmetrically.…”

Section: Betamentioning

confidence: 99%

“…Lin et al [30] proposed to monitor driving performance, that is, the driver's ability to maintain the vehicle in the middle of the road, using a linear regression model on a two-channel EEG. When the model is driven and tested on the same session, they get a correlation of r = 0.88 between their model and driving performance.…”

Section: Comparison With Literaturementioning

confidence: 99%

“…Thus, we see that this method needs to be parameterized for each driver since the model estimated on one driver does not necessarily work very well on another. Lin et al managed to improve these results by using an ICA on a 33-channel EEG [30] before applying their linear regression model. They then get a correlation of r = 0.88 between their estimate and the actual driving performance on the test session.…”

Section: Comparison With Literaturementioning

confidence: 99%

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Analysis of Driver Drowsiness Detection using EEG and EOG

Firke¹,

Jain²

2018

IJET

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This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.

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A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification

et al. 2019

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Due to the increasing attention to online learning, cognitive load has been recently considered as a crucial indicator for judging teenagers' learning state so as to improve both learning and teaching effects. However, some traditional cognitive load measurement methods such as subjective measurement are easily influenced by subjective sensation deviation of subjects. None of them can reflect the cognitive load of learners more precisely. Recently, machine learning-based data modeling has gained more importance in the scenarios of various smart wearables and Internet of things applications. Meanwhile, physiological signals have proven to contribute much to human health assessment. On the basis of the above considerations, this paper proposes a physiological data-driven model for learners' cognitive load detection under the application of smart wearables. The model consists of four modules: physiological signal acquisition, signal preprocessing, heart rate variability and pulse rate variability feature fusion, and cognitive load classification through an optimized extreme gradient boosting classifier in which hyperparameters are adaptively tuned with sequential model-based optimization. Furthermore, we design an experimental paradigm for signal acquisition in a learning environment, and the experimental results demonstrate that the proposed model for cognitive load detection outperforms conventional approaches that only employ either heart rate variability or pulse rate variability for modeling. We also compare the effects of different feature fusion algorithms combined with different classification algorithms, which demonstrates that the proposed model achieves the highest accuracy of cognitive load detection due to its optimal combination of feature fusion and classification.

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Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Cited by 107 publications

References 53 publications

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Analysis of Driver Drowsiness Detection using EEG and EOG

A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification

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