An Event-based User Experience Research On Virtual Reality Applications

With the rapid development of virtual reality(VR)technology in recent years,the VR industry is currently growing fast.Although VR provides many new possibilities for content form,due to the immaturity of VR application design,designers in the VR industry still face severe challenges.Design of VR applications still needs to be verified by users.Therefore,user experience analysis is really critical to the success of VR software development.At the same time,with the increase of users' requirements for VR content quality,whether the content is attractive enough will greatly affects users' experience and determine the service life of VR applications.Consequently,searching for interaction events that would affect VR user experience is critical to improve user stickiness.At present,the researches on VR mainly focus on the improvement of hardware and software,and pay less attention to the content event design.Moreover,there is no unified and clear standard for VR user experience evaluation.This paper attempts to find out the relationship between user traits,VR interaction events and user experience through realistic experiments.This paper firstly defines four types of VR interaction events,and design a questionnaire for collecting the tester's traits and their subjective evaluation.The 80 testers were divided into two groups to experience two types of VR games with constant time.During the experiment,objective physiological data of the testers and their participation process were recorded.The statistical method and improved Prism algorithm were used to find out the correlation among user traits,type of game interaction events and user experience.The experiment results could provide references for VR designers and developers,and at the same time provide the preliminary study to standardized VR user experience evaluation.In addition,although VR technology has been able to provide users with an excellent immersive experience,it still has some side effects that affect user experience.Among them,cybersickness is one of the side effects that most affect user experience,often times severe enough to cause significant physical discomfort to the users and the discontinuation of use.Research on cybersickness has been going on for many years.Many researchers have tried to find the theories and factors which contribute to this unpleasant experience,but they are in no shape to determine clearly the underlying contrivances of cybersickness.Many research works try to detect,explain,and limit the occurrence of this problem.However,most of current methods of detection and analysis rely on using subjective questionnaires gathering user's state a posteriori regarding several symptoms known to be a consequence of this sickness: dizziness,nausea,cold sweats,disorientation,eyestrain,etc.Although there are existing systems to quantify and measure cybersickness but a real-time prediction tool which can be used by application developers to evaluate their products for cybersickness susceptibility is non-existent.This work proposes to detect and quantify cybersickness in real time by applying deep learning methods using physiological sensors.We designed and developed a VR experimental platform and several passive navigation tasks to induce cybersickness.Then collected the user's real-time physiological signals during the experiment,including electrical skin activity(EDA)and electrocardiogram(ECG),as well as virtual avatars motion data,including position and bone rotation.After we trained a LSTM-Attention neural network model using the multi-source data set we collected.This model can detect the user's cybersickness level during their immersion in the virtual environment without asking real-time cybersickness feedback from them.A 5-fold cross-validation scheme was used to evaluate the performance validity of the model.Average accuracy of 96.58%was achieved for classification of level of cybersickness,showing great performance when being compared to other related studies in the field.The results show the feasibility of accurate classification of cybersickness using our cybersickness prediction model.The proposed model will also provide researchers and developers a chance to detect the severity of cybersickness of users in real-time.Then based on the severity level,they can apply different cybersickness reduction techniques to automatically and dynamically improve user experience.