Virtual reality sickness detection: an approach based on physiological signals and machine learning

Martin, Nicolas; Mathieu, N.; Pallamin, Nico; Ragot, Martin; Diverrez, Jean-Marc

doi:10.1109/ismar50242.2020.00065

Cited by 28 publications

(10 citation statements)

References 83 publications

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“…A brief description of each of the ML/AI technologies considered is provided in Table 11. ( Abdurrahman et al, 2021), (Bhalla et al, 2021), (De Bruyne et al, 2021), (Delvigne et al, 2022), (Delvigne et al, 2020), (Georgiou & Demiris, 2017), (Jones et al, 2020), (Liebers et al, 2021), (Leiker et al, 2016), (Migovich et al, 2021), (Noghabaei et al, 2021), (Porssut et al, 2022), (Shi et al, 2020), (Tsai et al, 2021), (Wilson et al, 2021), (Zhang et al, 2017) From (Porssut et (Granato et al, 2020), (Ihmig et al, 2020), (Islam et al, 2019), (Kim et al, 2019), (Luong et al, 2020), (Martin et al, 2020), (Migovich et al, 2021), (Sakib et al, 2020), (Sayis et al, 2022), (Tabbaa et al, 2022), (Tsai et al, 2021), (Wilson et al, 2021), (Zhang et al, 2017) From (Martin et al, 2020) (continued) (Bauer et al, 2019), (Bin Suhaimi et al, 2020), (Delvigne et al, 2022), (Delvigne et al, 2020), (Jones et al, 2020), (Kaur et al, 2019), (Kim et al, 2020), (McDermott et al, 2022), …”

Section: Ai/ml Uses and Definitions Of Approachesmentioning

confidence: 99%

“…++ (Sakib et al, 2022), (Suhaimi et al, 2021) (continued) A gradient boost machine is a type of machine learning technique that uses an ensemble of weak prediction models to perform regression and classification tasks. ++ (Abujelala et al, 2021), (Bin Suhaimi et al, 2020), (Granato et al, 2020), (Martin et al, 2020), (Recenti et al, 2021), (Suhaimi, et al, 2022) K-Nearest Neighbour (KNN)…”

Section: Feedforward Artificial Neural Network (Fann)mentioning

confidence: 99%

“…++ (Abujelala et al, 2021), (Stranick & Lopez, 2022), (Tsai et al, 2021) Naive Bayes (NB) A family of simple probabilistic classifiers based on applying Bayes' theorem with strong independence assumptions between the features. + (Abujelala et al, 2021), (Bin Suhaimi et al, 2020), (Stranick & Lopez, 2022), (Suhaimi, et al, (Abujelala et al, 2021), (Bhalla et al, 2021), (Dwivedi et al, 2020), (Granato et al, 2020), (Luong et al, 2020), (Martin et al, 2020), (Oh & Kim, 2021), (Recenti et al, 2021), (Suhaimi, et al, 2022), (Wilson et al, 2021) (continued) addition to being used in primary data analysis, these biosignals are useful as a secondary source to support machine learning approaches (Cho et al, 2017;Jones et al, 2020;Martin et al, 2020). The generation and distribution of the relevant datasets will be important for growth in the use of these approaches.…”

Section: Feedforward Artificial Neural Network (Fann)mentioning

confidence: 99%

“…As such, details regarding integration of biosensing technologies with synthetic environment development tools (i.e., game engines) should be a focus of development approaches. The Unity game engine was used in several studies (including De Bruyne et al, 2021;Delvigne et al, 2022;Dwivedi et al, 2020;Marín-Morales et al, 2021; (Abdurrahman et al, 2021), (Abujelala et al, 2021), (Bhalla et al, 2021), (Bui et al, 2019), (De Bruyne et al, 2021), (Granato et al, 2020), (Kim et al, 2019), (Martin et al, 2020), (Marín-Morales et al, 2021), (Noghabaei et al, 2021), (Oh & Kim, 2021), (Recenti et al, 2021), (Rodrigues et al, 2021), (Stranick & Lopez, 2022), (Suhaimi et al, 2017), (Suhaimi et al, 2020), (Suhaimi, et al, 2022), (Tabbaa et al, 2022), (Zhang et al, 2017) + From "The Appen Artificial Intelligence Glossary", https://appen. Porssut et al, 2022;Rodrigues et al, 2021;Stranick & Lopez, 2022;Tsai et al, 2021) and the Unreal Engine 4 was used in only one study from the 48 studies included (McDermott et al, 2022).…”

Section: Feedforward Artificial Neural Network (Fann)mentioning

confidence: 99%

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Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Blackmore,

Smith,

Bailey

et al. 2024

Simulation & Gaming

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Background The addition of biofeedback and artificial intelligence (AI) in simulation training and serious games has shown promising results in improving the effectiveness of training and can lead to increased engagement, motivation, and retention of information. This systematic literature review explores the integration of biofeedback and artificial intelligence into eXtended reality (XR) training scenarios and is the first review to provide a consolidated overview of applied biofeedback and AI technologies in this area. Method This review was conducted using keywords related to biofeedback, AI, XR, and training and included papers that: contained the use of biofeedback and AI in XR training scenarios; reported on at least one outcome related to training effectiveness; were published in English; were peer-reviewed; date from 1 January 2016 – 7 February 2022. Results The results indicate that many studies collect two or more biosignals using a single biosensing device. This is particularly relevant in applied settings, where ease of use and minimal interference in training/education activities is desired. Also, that light, portable devices such as wrist bands, wireless straps, or headbands are preferred. Additionally, eye tracking, electrodermal activity (EDA), and photoplethysmograms (PPG) present as particularly useful biomarkers of stress and/or cognitive load in XR training contexts. A wide variety of machine learning (ML) approaches were used to support biofeedback systems in XR environments. However, a limited number of studies employed real-time analysis of biosignals (just 1% of studies) which indicates current challenges in implementing such systems. Conclusion The majority of papers meeting the selection criteria were from the fields of education and healthcare. Further research in other domains, such as defense and general industry, is needed to gain a comprehensive understanding of the potential for biofeedback and AI integration in XR training scenarios used in these domains.

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Section: Ai/ml Uses and Definitions Of Approachesmentioning

confidence: 99%

Section: Feedforward Artificial Neural Network (Fann)mentioning

confidence: 99%

Section: Feedforward Artificial Neural Network (Fann)mentioning

confidence: 99%

Section: Feedforward Artificial Neural Network (Fann)mentioning

confidence: 99%

See 2 more Smart Citations

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Blackmore,

Smith,

Bailey

et al. 2024

Simulation & Gaming

View full text Add to dashboard Cite

show abstract

“…Physiological signals can also be used directly to control user actions [15] or other parameters. For example, PPG could be change effort levels during physically active games and EDA to automatically trigger VR comfort options on cybersickness onset [16]. Questions of relevance include:…”

Section: E Exploring Uses For Physiological Sensing and Biofeedbackmentioning

confidence: 99%