Virtual Reality Racket Sports: Virtual Drills for Exercise and Training

Liu, Huimin; Wang, Zhiquan; Mousas, Christos; Kao, Dominic

doi:10.1109/ismar50242.2020.00084

Cited by 41 publications

(9 citation statements)

References 68 publications

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“…In (d), the control group did not conduct training significantly from pre-to the post-test, and the level of improvement could keep up, indicated by no significant difference between the post-and retention tests. Positive transfer of VR-adapted skills into real world could be found previously in football (Huang et al 2015), rowing (Hoffmann et al 2014), table tennis (Liu et al 2020) and further, but not reported in combat sports (Petri et al 2019c). Nevertheless, we have to reject the first hypothesis, in which we assumed higher learning for the VR training groups than the VB.…”

Section: Discussionmentioning

confidence: 60%

“…Apart from a few exceptions, for example (Liu et al 2020), which dealt with both, most of the previous enumerated studies examined the execution of motoric tasks. The natural locomotion realizable within HMD-based VR scenarios ensures relative natural body movements (taking one step forward leads to an adequate positional shifting in VR), essential considering sports activities.…”

Section: Background and Current Issuesmentioning

confidence: 99%

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Training in virtual reality enables learning of a complex sports movement

et al. 2022

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Despite the increased use in sports, it is still unclear to what extent VR training tools can be applied for motor learning of complex movements. Previous VR studies primarily relate to realize performances rather than learning motor skills. Therefore, the current study compared VR with video training realizing the acquisition of karate technique, the Soto Uke moving forward in Zenkutsu Dachi, without being accompanied by a trainer or partner. Further analyses showed whether a less lavished forearm compared to a whole-body visualization in VR is necessary to acquire movements’ basics sufficiently. Four groups were tested: 2 groups conducted VR training (VR-WB: whole-body visualization, and VR-FA having only visualized the forearms), the third group passed through a video-based learning method (VB), and the control group (C) had no intervention. In consultation with karate experts, a scoring system was developed to determine the movements’ quality divided, into upper- and lower body performance and the fist pose. The three-way ANOVA with repeated measurements, including the between-subject factor group [VR-WB, VR-FA, VB, C] and the within-subject factors time [pre, post, retention] and body regions [upper body, lower body, fist pose], shows that all groups improved significantly (except for C) with the similar course after four training sessions in all body regions. Accordingly, VR training seems to be as effective as video training, and the transfer from VR-adapted skills into the natural environment was equally sufficient, although presenting different body visualization types. Further suggestions are made related to the features of future VR training simulations.

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Section: Discussionmentioning

confidence: 60%

Section: Background and Current Issuesmentioning

confidence: 99%

Training in virtual reality enables learning of a complex sports movement

et al. 2022

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“…With the recent development of emerging metaverse technologies, the application fields of VR have been expanding rapidly. Nowadays, BCI technologies are actively studied as a new modality of communication in various VR applications ( Coogan and He, 2018 ), including rehabilitation ( Karamians et al, 2020 ), medical training ( Singh et al, 2020 ), entertainment ( Liu et al, 2020 ), and education ( Sundaram et al, 2020 ). Our proposed hybrid BCI technology that outperformed the conventional SSVEP-based BCI in terms of classification accuracy and ITR is expected to be used as an important tool for hands-free controlling of VR environments especially for those who cannot freely move their limbs.…”

Section: Discussionmentioning

confidence: 99%

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain–Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate

Park

2022

Front. Neuroinform.

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Brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a “hands-free” manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user’s horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

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“…those who carry them out (e.g., phobias treatment), giving the possibility of exploring human behavior in its virtual analog without compromising its integrity [3]. Over the years, multiple applications of VRS have been developed, ranging from education [4], [5], sports training [6], [7], driving [8], [9], architecture [10], [11], and video games [12], [13], to more complex ones such as rehabilitation [14], [15], military training [16], manufacturing [17], [18], and medical training [19]- [21]. Additionally, VRS can be used in psychology as an alternative therapy to treat patients with phobias, anxiety, and Post-Traumatic Stress Disorder (PTSD) [2], [22].…”

Section: Introductionmentioning

confidence: 99%

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

et al. 2022

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Virtual Reality (VR) video games have become popular in recent years with the emergence of a wide variety of enhanced VR hardware systems. Typically, current VR video games incorporate high-quality auditory and video feedback along with vibro-tactile cues to provide an immersive experience. However, the ongoing VR hardware and software do not consider the state of the user to determine whether the video game is generating an enjoyable experience, or if it has become a stressing or boring experience. This work provides an assessment of stress level estimation from features extracted from Electrocardiography (ECG), Electrodermal Activity (EDA), and Electromyography (EMG) signals of users while playing a VR video game with different difficulty levels. Statistical differences were found between the rest and gaming stages for several extracted features. Regardless of the fact that no significant statistical differences between the three levels of difficulty were found by analyzing the EDA and ECG features, an 83.1% accuracy was obtained for the classification between the three levels with a KNN model. For the EMG signal, the obtained accuracy ranged between 99% and 100% for the distinction between a difficult level 1 and rest stage for all models. When all features from the ECG, EDA and EMG signals were used, an accuracy of 99% was obtained for the differentiation between the three difficulty levels and a resting stage. The presented work results may serve as a reference for future work regarding feature extraction and game difficulty adaptation for user experience enhancement.

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Virtual Reality Racket Sports: Virtual Drills for Exercise and Training

Cited by 41 publications

References 68 publications

Training in virtual reality enables learning of a complex sports movement

Training in virtual reality enables learning of a complex sports movement

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain–Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

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