VR Motion Research Articles

A Siam-LSTM Model for Multichannel EEG on VR Motion Sickness Recognition

A Siam-LSTM Model for Multichannel EEG on VR Motion Sickness Recognition

2D4-5 The Influence of Musical Tempo on VR Motion Sickness Induced by Visual Rotational Stimuli

2D4-5 The Influence of Musical Tempo on VR Motion Sickness Induced by Visual Rotational Stimuli

VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research.

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches would certainly benefit from an accurately labeled, real-world, diverse dataset that enables the development of generalizable ML models. We introduce 'VR.net', a dataset comprising 165-hour gameplay videos from 100 real-world games spanning ten diverse genres, evaluated by 500 participants. VR.net accurately assigns 24 motion sickness-related labels for each video frame, such as camera/object movement, depth of field, and motion flow. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we implement a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.We also provide access to our data collection tool, enabling researchers to contribute to the expansion of VR.net.

Progress of Objective Evaluation Methods for VR Motion Sickness based on EEG

Progress of Objective Evaluation Methods for VR Motion Sickness based on EEG

A VR User Behavior Classification Method Integrating Scene Information and Operation Information

With the rapid growth of the virtual reality industry, the number of users participating in virtual reality has rapidly increased, and analyzing user behavior in virtual reality scenarios has become increasingly important. Compared with general time series classification tasks, the input sources in virtual reality are more complex and diverse, and traditional time series classification models are difficult to handle this complexity. This article designs a series of methods for classifying VR user behavior and conducts experiments. Apply video motion recognition methods to VR motion recognition based on VR scene data. Due to the ability to obtain a series of image data from both videos and scenes, this article uses the method of video action recognition to process VR scene data. On this basis, a series of fusion methods were designed to fuse the operational information of VR. Experiments have shown that these methods can effectively handle action recognition problems in VR scenes.

Approach to Knee Arthropathy through 180-Degree Immersive VR Movement Visualization in Adult Patients with Severe Hemophilia: A Pilot Study.

(1) Background: Hemarthrosis is a typical clinical manifestation in patients with hemophilia. Its recurrence causes hemophilic arthropathy, characterized by chronic joint pain. Watching movement recorded from a first-person perspective and immersively can be effective in the management of chronic pain. The objective of this study was to evaluate the effectiveness of an immersive virtual reality intervention in improving the pain intensity, joint condition, muscle strength and range of motion in patients with hemophilic knee arthropathy. (2) Methods: Thirteen patients with hemophilic knee arthropathy were recruited. The patients wore virtual reality glasses and watched a flexion–extension movement of the knee on an immersive 180° video, recorded from a first-person perspective over a 28-day period. The primary variable was the pain intensity (visual analog scale). The secondary variables were the joint status (Hemophilia Joint Health Score), quadriceps and hamstring strength (dynamometry), and range of motion (goniometry). (3) Results: After the intervention period, statistically significant differences were observed in the intensity of the joint pain (Standard error [SE] = 19.31; 95% interval confidence [95%CI] = −1.05; −0.26), joint condition (SE = 18.68; 95%CI = −1.16; −0.52) and quadriceps strength (SE = 35.00; 95%CI = 2.53; 17.47). We found that 38.46% and 23.07% of the patients exhibited an improvement in their quadriceps muscle strength and joint condition above the minimum detectable change for both variables (8.21% and 1.79%, respectively). (4) Conclusions: One hundred and eighty degree immersive VR motion visualization can improve the intensity of joint pain in patients with hemophilic knee arthropathy. An intervention using immersive virtual reality can be an effective complementary approach to improve the joint condition and quadriceps strength in these patients.

180-degree immersive VR motion visualization in the treatment of haemophilic ankle arthropathy.

Patients with haemophilic arthropathy suffer chronic pain that affects and restricts their quality of life. Visualization of movement through immersive virtual reality is used for pain management. To evaluate the efficacy of 180-degree immersive VR motion visualization therapy in patients with haemophilic ankle arthropathy. Prospective, multicentre pilot study. Fifteen adult patients with bilateral haemophilic ankle arthropathy were recruited (mean age: 42.73±12.36 years). The intervention lasted 4weeks, with daily home sessions of 180-degree immersive motion visualization. The patients were given virtual reality glasses to use with their smartphones. From the YouTube mobile app® they accessed the recorded video with access from the He-Mirror App®. The study variables were joint state (Haemophilia Joint Health Score), pressure pain threshold (pressure algometer), muscle strength (dynamometry) and range of motion (goniometry). Three evaluations were performed: at baseline (T0), after the intervention (T1) and at the end of a 16-week follow-up period (T2). No patient developed ankle hemarthrosis during the experimental phase. In the repeated measures analysis we found statistically significant differences in joint state (F=51.38; η2 p =.63), pressure pain threshold of the lateral malleolus (F=12.34; η2 p =.29) and range of motion (F=11.7; η2 p =.28). Therapy using immersive motion visualization does not cause hemarthrosis. This intervention can improve joint condition, pressure pain threshold and range of motion in patients with ankle arthropathy. Changes greater than the MDC were reported in more than 40% of patients for the variables pressure pain threshold, anterior tibialis strength and range of motion, which were considered clinically relevant.

VR motion sickness recognition by using EEG rhythm energy ratio based on wavelet packet transform

Background and objectivesVirtual reality motion sickness (VRMS) is one of the main factors hindering the development of VR technology. At present, the VRMS recognition methods using electroencephalogram (EEG) signals have poor applicability to multiple subjects. MethodsAiming at this dilemma, the wavelet packet transform (WPT), was used to propose a feature extraction method for EEG rhythm energy ratios of delta (δ), theta (θ), alpha (α), and beta (β) in this research. Moreover, VRMS was recognized by combining k-Nearest Neighbor classifier (k-NN), support vector machine (SVM) with polynomial kernel (polynomial-SVM) and radial basis function kernel (RBF-SVM), respectively. The method is that the raw EEG signals were de-noised by an elliptical band-pass filter and segmented by a fixed window, 7-level db4 WPT was performed on each EEG segment, and the wavelet packet energy ratios of delta, theta, alpha and beta rhythms from FP1, FP2, C3, C4, P3, P4, O1 and O2 channels were calculated and combined to form feature vectors for recognizing VRMS. ResultsUnder the condition of 4-s window size, the average VRMS recognition accuracy of polynomial-SVM for the single subject was 92.85%, and the VRMS recognition accuracy of 18 subjects was about 79.25%. ConclusionsCompared with other VRMS recognition methods, this method does not only have a higher recognition accuracy to a single subject, but also have better applicability to multiple subjects. Meanwhile, when using the EEG four rhythm energy ratios of FP1, FP2, C3, C4, P3, P4, O1 and O2 channels as feature vectors, the polynomial-SVM achieved better VRMS recognition performance than the k-NN and RBF-SVM.