Al-Ashwal, Wadhah, Asadi, Houshyar, Chalak Qazani, Mohamad Reza, Mohamed, Shady, Nahavandi, Saeid, and Riecke, Bernhard (2024) Galvanic Skin Response and AE-LSTM for Anomaly Detection in VR-Induced Motion Sickness. In: Proccedings of the 2024 IEEE International Systems Conference. -7. From: SysCon: 2024 IEEE International Systems Conference, Montreal, Canada, 15-18 April 2024.

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Abstract

his study advances the understanding of motion sickness (MS) by integrating subjective measures, like the simulator sickness questionnaire (SSQ), with objective physiological metrics, particularly galvanic skin response (GSR), analysed through an Autoencoder Long Short-Term Memory (AE-LSTM) model. This model, designed for unsupervised anomaly detection, evaluates GSR data to detect differences in physiological (GSR) responses to conditions of different MS potential (here different weather scenarios in a naturalistic VR helicopter simulation). By comparing these physiological anomalies with self-reported cybersickness scores, our findings highlight the importance of combining machine learning-analysed physiological data with subjective reports, offering a comprehensive approach to assessing MS in different conditions. The transition from clear to stormy scenarios revealed marked elevations in MS scores, although the model was currently not able to reliably identify scenario-specific physiological responses that correlate with increased MS.

Item ID:	87056
Item Type:	Conference Item (Research - E1)
ISBN:	979-8-3503-5880-3
Copyright Information:	© 2024, IEEE.
Funders:	Australian Research Council (ARC)
Date Deposited:	28 Aug 2025 01:50
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 75% 42 HEALTH SCIENCES > 4299 Other health sciences > 429999 Other health sciences not elsewhere classified @ 25%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 80% 20 HEALTH > 2002 Evaluation of health and support services > 200208 Telehealth @ 20%
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