Sistem pelacakan posisi pengguna menggunakan marker-based AR dalam menjelajahi galeri museum VR

This study examines the user position tracking system using marker-based AR on smartphones camera. The tracking system uses a homographic algorithm integrated into the Galeri Museum VR application. In the test, the user performed exploration interactions by 6 degrees of freedom in ten different positions in the museum gallery. The physical space used in this study was 4 x 4 m2 and a marker attached to the wall in front of the user. This system results in errors in XYZ field (0.102 m, 0.047 m, 0.044 m). If the camera's orientation is not directing to the marker and the user is moving, jitter appears because of the untracked marker. The use of marker-based AR successfully applied to track the position of users who perform natural locomotion interactions in the VR environment.Penelitian ini mengkaji sistem pelacakan posisi pengguna menggunakan marker-based AR pada smartphone berkamera. Sistem pelacakan menggunakan pengembangan algoritme homografi yang diintegrasikan ke aplikasi Galeri Museum VR. Pengujian dilakukan pada pengguna yang melakukan interaksi penjelajahan dengan 6 derajat kebebasan di sepuluh posisi yang berbeda di galeri museum. Ukuran ruangan fisik yang digunakan dalam pengujian adalah 4 x 4 meter persegi dan satu penanda ditempel pada dinding di depan pengguna. Sistem ini mampu menghasilkan galat bidang XYZ (0,102 m, 0,047 m, 0,044 m). Jika orientasi kamera tidak mengarah ke penanda dan pengguna bergerak, muncul jitter karena penanda tidak terlacak. Penggunaan marker-based AR dapat diterapkan untuk melacak posisi pengguna yang melakukan interaksi perpindahan posisi di lingkungan VR

Immersive virtual reality or computerised mindfulness meditation for improving mood? Preliminary efficacy from a pilot randomised trial

Introduction: Mindfulness interventions are effective in improving mood, reducing stress, and increasing quality of life. New developments in technology bring important channels to deliver mindfulness interventions that can increase accessibility, such as the Internet, computerised interventions, mobile apps and recently, virtual reality (VR). The aim of the present study is to enhance our current understanding of the use of VR in mindfulness, namely we examined in a pilot randomised trial the efficacy of an immersive VR-based mindfulness approach compared to an active control (computerised-based mindfulness meditation) on improving mood. A secondary objective was to examine whether VR use resulted in simulator sickness which could affect user engagement.Methods: Forty-seven (Mage = 29.22 years) healthy participants were randomly assigned to the experimental or control group.Results: A mixed 2X3 ANOVA showed a significant Time effect. Namely, negative emotions were reduced in both groups, with non-significant differences between groups. For positive emotions, on the other hand, our results showed no significant impact. Simulator sickness in VR was not present, according to t-test, making VR a safe delivery method.Discussion: Future research should investigate VR dosage and combine VR with other interventions (e.g., blended with face-to-face mindfulness interventions, with Internet-delivered interventions)

Perception-based high quality distributed virtual reality

Virtual reality has great potential to enable remote collaborative work from anywhere in the world. Developing virtual reality into a platform suitable for natural interaction and immersive collaboration requires the experience to be reliably stable. For a networked collaborative environment, perceived smoothness of motion is limited by the tick rate, that is, the frequency at which information is distributed. As tick rate increases, motion will appear increasingly smooth; however, excessive tick rates may introduce additional load on a network without any perceptible benefit to a user. This paper details two visual psychophysics experiments (N1=16, N2=11) carried out to evaluate participant sensitivity to tick rate in virtual reality. The influence of three variables, velocity, complexity, and digital medium were investigated. Both velocity and digital medium displayed a significant effect, whilst complexity did not show significance. A model was then built and validated from the results of these experiments. The model predicts for average walking speed within the desktop condition, that 90% of the population will perceive motion to be smooth at 56 Hz, whilst this 90% threshold lies at 113 Hz for the VR condition. This model can predict participant perception of tick rate under given conditions, enabling networks to intelligently optimise participant experience without adding unnecessary further load on the network

Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Evaluation of Detecting Cybersickness via VR HMD Positional Measurements Under Realistic Usage Conditions.

With the resurgence of virtual reality, head-mounted displays (VR HMD) technologies since 2015, VR technology is becoming ever more present in people's day-to-day lives. However, one significant barrier to this progress is a condition called cybersickness, a form of motion sickness induced by the usage of VR HMD’s. It is often debilitating to sufferers, resulting in symptoms anywhere from mild discomfort to full-on vomiting. Much research effort focuses on identifying the cause of and solution to this problem, with many studies reporting various factors that influence cybersickness, such as vection and field of view. However, there is often disagreement in these studies' results and comparing the results is often complicated as stimuli used for the experiments vary wildly. This study theorised that these results' mismatch might partially be down to the different mental loads of these tasks, which may influence cybersickness and stability-based measurement methods such as postural stability captured by the centre of pressure (COP) measurements. One recurring desire in these research projects is the idea of using the HMD device itself to capture the stability of the users head. However, measuring the heads position via the VR HMD is known to have inaccuracies meaning a perfect representation of the heads position cannot be measured. This research took the HTC Vive headset and used it to capture the head position of multiple subjects experiencing two different VR environments under differing levels of cognitive load. The design of these test environments reflected normal VR usage. This research found that the VR HMD measurements in this scenario may be a suitable proxy for recording instability. However, the underlying method was greatly influenced by other factors, with cognitive load (5.4% instability increase between the low and high load conditions) and test order (2.4% instability decrease between first run and second run conditions) having a more significant impact on the instability recorded than the onset of cybersickness (2% instability increase between sick and well participants). Also, separating participants suffering from cybersickness from unaffected participants was not possible based upon the recorded motion alone. Additionally, attempts to capture stability data during actual VR gameplay in specific areas of possible head stability provided mixed results and failed to identify participants exhibiting symptoms of cybersickness successfully. In conclusion, this study finds that while a proxy measurement for head stability is obtainable from an HTC Vive headset, the results recorded in no way indicate cybersickness onset. Additionally, the study proves cognitive load and test order significantly impact stability measurements recorded in this way. As such, this approach would need calibration on a case-by-case basis if used to detect cybersickness