A Study on Sensor System Latency in VR Motion Sickness

Abstract

One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error.

Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays

“…Unique challenges are presented by 360 video streaming. The occurrence of lagging and stalling leads to motion sickness [48] that breaks the immersiveness [49]. Therefore, high-performance streaming system is important to maintain users' QoE.…”

Validate the Users’ Comfortable Level in the Virtual Reality Walkthrough Environment for Minimizing Motion Sickness

“…There are five main factors that contribute to VR motion sickness: motion and speed, field of view, latency, duration of use, and environment. Rapid and intense movements [18][10] [19], a narrow field of view [12][20][21], high latency [22][23] [24], prolonged use [25][26] [27], and overly stimulating environments [28][29] [30] can trigger motion sickness in VR users. By understanding these factors and taking steps to mitigate them, it is possible to reduce the risk of VR motion sickness and improve the VR experience for users.…”

Electroencephalography Signal Analysis for Virtual Reality Sickness: Head-mounted Display and Screen-based

“…All EEG frequency bands were analyzed in a related study, resulting in various conclusions regarding the relationship between EEG signals and VR sickness [39]. Alpha (8)(9)(10)(11)(12)(13) and low beta (13)(14)(15)(16)(17)(18)(19)(20)(21) waves are interesting to be explicitly analyzed because of the various frequency bands of these brain waves. Alpha waves occur in a conscious and relaxed person with closed eyes.…”