An Evaluation of Extrapolation and Filtering Techniques in Head Tracking for Virtual Environments to Reduce Cybersickness

García-Agúndez, Augusto; Westmeier, Aiko; Caserman, Polona; Konrad, Robert Arthur; Göbel, Stefan

doi:10.1007/978-3-319-70111-0_19

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…Numerous potential solutions to CS have been discussed, e.g., a virtual nose (Whittinghill et al 2015;Wienrich et al 2018) or motion sickness medication (Chen et al 2015). Many previous studies suggest different methods to reduce the discrepancy between virtual and real movements, such as restricting movement to instantaneous locomotion (Christou and Aristidou 2017), manipulating the limited physical VR space with acoustic redirected walking (Nogalski and Fohl 2016), or extrapolating and filtering head movements (Garcia-Agundez et al 2017). Including environmentally meaningful features such as sound and vibration when operating a virtual vehicle (Sawada et al 2020) or snapping the viewpoint in sections of significant movement (Farmani and Teather 2018) have also been mentioned.…”

Section: Introductionmentioning

confidence: 99%

Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook

et al. 2021

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Cybersickness (CS) is a term used to refer to symptoms, such as nausea, headache, and dizziness that users experience during or after virtual reality immersion. Initially discovered in flight simulators, commercial virtual reality (VR) head-mounted displays (HMD) of the current generation also seem to cause CS, albeit in a different manner and severity. The goal of this work is to summarize recent literature on CS with modern HMDs, to determine the specificities and profile of immersive VR-caused CS, and to provide an outlook for future research areas. A systematic review was performed on the databases IEEE Xplore, PubMed, ACM, and Scopus from 2013 to 2019 and 49 publications were selected. A summarized text states how different VR HMDs impact CS, how the nature of movement in VR HMDs contributes to CS, and how we can use biosensors to detect CS. The results of the meta-analysis show that although current-generation VR HMDs cause significantly less CS ($$p<0.001$$ p < 0.001 ), some symptoms remain as intense. Further results show that the nature of movement and, in particular, sensory mismatch as well as perceived motion have been the leading cause of CS. We suggest an outlook on future research, including the use of galvanic skin response to evaluate CS in combination with the golden standard (Simulator Sickness Questionnaire, SSQ) as well as an update on the subjective evaluation scores of the SSQ.

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Section: Introductionmentioning

confidence: 99%

Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook

et al. 2021

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“…This extrapolation method has been used for recent commercialized VR headsets, such as Oculus Rift [30]. Another study proved that linear extrapolation outperforms the polynomial extrapolation method [19]. In terms of MAE, extrapolation that only uses IMU sensor data outperformed the other model with sEMG and HD-EMG data.…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation in this study was the prediction time, which was only 13 ms in the future. Thus, for VR systems that have latency greater than 13 ms, this result will not be useful [19].…”

Section: Related Studiesmentioning

confidence: 99%

“…To sum up, state-of-the-art VR head orientation prediction still has limitations in predicting high-intensity and abrupt movements when the user moves at a high speed and changes his or her movement direction abruptly. Moreover, how far in advance the system can predict future head orientations is another limitation that needs to be improved since the best result with an error of less than 1˚ achieved only a 13 ms prediction time [19]. Moreover, head movement prediction with EMG data still has problems with model generalization and electrode placement, as electrodes need to be placed precisely on the muscle belly when using an sEMG sensor.…”

Section: Related Studiesmentioning

confidence: 99%

“…To compare our results with state-of-the-art head orientation prediction, we used the linear extrapolation method to predict the pitch, roll, and yaw of the head orientation. This linear extrapolation is the baseline for the head orientation predictions used on VR devices such as the Oculus Rift [30] and tested in other studies [19]. The formula for linear extrapolation is provided in 7:…”

Section: ) Model Trainingmentioning

confidence: 99%

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Surface EMG vs. High-Density EMG: Tradeoff Between Performance and Usability for Head Orientation Prediction in VR Application

et al. 2021

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Head orientation prediction is one of the solutions to reduce end-to-end latency on Virtual Reality (VR) systems and is important since it can alleviate negative effects like motion sickness. This study compared head orientation prediction models from two different electromyography (EMG) systems: surface EMG (sEMG) and High-Density EMG (HD-EMG). The deep learning method was used to train the prediction model, and the results showed that the model with input from the pre-processed sEMG + IMU sensor outperformed the model with input from the HD-EMG + IMU sensor. However, the decreasing performance from HD-EMG was compensated by its comfort and the ease of use of its electrode. This tradeoff between performance and usability with sEMG compared to HD-EMG should be a consideration for users who want to choose between performance and ease of use for head orientation prediction purposes. Comparison with state-of-the-art head prediction methods proved that the sEMG-based model offers better performance in predictions when users change their head directions, which was quantified by calculating the dt peaks. In other words, our sEMG-based prediction model is suitable for VR applications, which require the user to perform high-intensity or abrupt movements, such as in FPS games or exercise/sports games. INDEX TERMS deep neural network, head orientation prediction, high-density electromyography, lowlatency virtual reality, surface electromyography.

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Software Techniques to Reduce Cybersickness Among Users of Immersive Virtual Reality Environments

Choroś

Nippe

2019

Intelligent Information and Database Systems

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An Evaluation of Extrapolation and Filtering Techniques in Head Tracking for Virtual Environments to Reduce Cybersickness

Cited by 12 publications

References 22 publications

Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook

Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook

Surface EMG vs. High-Density EMG: Tradeoff Between Performance and Usability for Head Orientation Prediction in VR Application

Software Techniques to Reduce Cybersickness Among Users of Immersive Virtual Reality Environments

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