VR-Based Training Game to Support the Practice of Forehand and Backhand Table Tennis Techniques Among Vietnamese University Students

Ⅰ. Introduction

Table tennis is a common subject taught in physical education programs at Vietnamese universities. At the same time, numerous studies have been carried out to examine different aspects of the sport, with the aim of supporting teaching practices and hands-on training for students. A study conducted at Saigon University indicated that while students possess basic knowledge of table tennis, their active engagement in practical training remains limited due to traditional teaching methods[1]. Similarly, research at Vietnam National University, Hanoi, proposed innovative teaching methods to support students in transitioning away from traditional passive learning approaches[2].

Leveraging the advantages of 3D technology over 2D, a digital tool has been developed to support the learning of table tennis techniques for students at Thai Nguyen University of Education[3]. However, the application of VR technology in table tennis training within Vietnamese higher education remains limited and has not yet been widely recognized or implemented across universities.

Table tennis serves as a vital component in enhancing physical fitness among university students in Vietnam. Despite its potential benefits, student engagement in the sport is notably low, influenced by several factors. These include suboptimal training environments, a shortage of adequate facilities and equipment, and a lack of diverse and dynamic pedagogical approaches that resonate with student interests and learning styles. Such limitations hinder the effective promotion of table tennis as a viable fitness activity within academic settings[1]. Therefore, research aimed at advancing teaching methods that support student engagement is crucial in the educational field.

Table tennis is a fast-paced sport that demands both physical strength and technical skill in each stroke executed by players[4]. In modern table tennis, mastering both forehand and backhand techniques is crucial. Typically, the backhand stroke generates less power than the forehand, as it relies primarily on the hips, forearm, and wrist for energy[5]. In contrast, the forehand stroke is generally more forceful, as it incorporates body flexion, shoulder rotation, and trunk movement[6]. Accordingly, researchers in [7] have shown that players often perform forehand strokes with greater proficiency than backhand strokes, making the forehand more suitable for aggressive play due to its extended range and higher power.

Previous research has shown that complex motor skills learned through VR training can effectively translate to real-world performance. Participants who trained with VR systems have demonstrated significant enhancements in both their technique and gameplay abilities[8],[9]. VR training has also been reported to outperform traditional methods in measures such as serve accuracy, stroke endurance, and overall skill ratings[10]. One of the outstanding benefits of VR is the ability to create engaging and gamified experiences, which boost learner motivation, satisfaction, and long-term interest in the training process[10]. These findings suggest that VR offers an exciting and potentially game-changing method for table tennis education and sports training. It may help address limitations associated with conventional methods, such as in-person coaching and video analysis. Previous studies[9]-[11] have also indicated that VR technology has the potential to support the acquisition of essential skills, including forehand and backhand techniques.

In addition, ball control is a key component in executing effective strokes. Therefore, this study incorporates the design and simulation of a virtual training model called “spin wheels”, inspired by the “Huieson Table Tennis Stroke Training” tool[12]. This model enables students to refine accuracy and precision by practicing with static targets rather than fast moving balls.

This study develops a VR-based training game application to support the development of muscle memory, which is critical for students’ quick reflexes and consistent technique. The system provides detailed visual guidance for right-handed university students practicing forehand and backhand table tennis techniques.

Ⅱ. Literature Review

The application of VR in sports training has gained significant popularity, particularly as research has demonstrated its advantages over traditional training methods. Studies have shown that complex motor skills acquired in VR environments can transfer effectively to real-world settings[8],[10]. Moreover, a notable strength of VR is its ability to simulate customizable environments and incorporate stimulating effects that boost user engagement and enjoyment during practice[13],[14].

Muscle memory is a crucial factor in skill development, enhancing reflexes, and boosting performance in sports[15]. Theoretically, this concept aligns with Fitts and Posner’s three-stage model of motor learning[16], which outlines the progression from the cognitive stage (understanding the mechanics), to the associative stage (refining movement through feedback), and finally to the autonomous stage (automatic execution). In table tennis, muscle memory is developed through consistent practice with correct posture and technique, particularly when players focus on replicating movements along a predefined trajectory model[17]. This approach has been effectively applied in VR-based training environments, where experiences closely simulate real-world practice[15].

The concept of gameplay naturally involves stimulation, engagement, and intrinsic motivation. This engagement can be explained by Self-Determination Theory (SDT)[18], which suggests that motivation is sustained when an activity satisfies the psychological needs for competence (feeling effective), autonomy (feeling in control), and relatedness (feeling connected). Previous studies have shown that players' motivation and engagement are positively influenced by psychological satisfaction and enjoyment[19]-[21]. In-game mechanics, such as varying levels of difficulty and point accumulation systems, play a significant role in stimulating the desire to engage and achieve success[22], effectively addressing the need for competence. Moreover, cognitive skills and self-regulation have been shown to play an important roles in promoting autonomous learning. Studies have confirmed that the use of game-based learning approaches as effective tools for promoting active and self-directed learning behaviors[23]-[25].

Existing approaches can be broadly categorized into three groups. One prominent direction involves VR-based systems that focus on creating immersive training environments combined with basic motion tracking and performance feedback[9]. While these systems improve engagement, they often lack detailed guidance on movement execution. Another school of thought focuses on some studies integrate wearable sensors or motion capture technologies to provide more precise kinematic feedback[8]. Despite the enhanced performance analysis offered by these systems, their complex setups often hinder usability. In contrast to these immersive environments, some traditional approaches emphasize foundational methods like shadow practice to build muscle memory, which provides a theoretical basis for modern AR and VR guidance but lacks the high-level immersion of fully digital systems[26].

As summarized in Table 1, most prior studies primarily provide general visual feedback or performance metrics, without explicitly representing motion trajectories in a way that is easily interpretable for learners. Furthermore, many studies focus on system implementation rather than examining the relationship between user experience and performance outcomes.

Table 1.

Comparison with related studies on VR-based sports training

Motivated by the lack of trajectory guidance in current VR tools, we developed a VR-based training system with explicit racket trajectory visualization. By directly visualizing stroke paths, the system aims to support students in understanding motion patterns more effectively. In addition, this study investigates the association between user experience and performance, contributing further insight into the role of engagement in VR-based training contexts.

Ⅲ. System Design

3-1 Game Workflow

Numerous flow frameworks have emerged from game-based training contexts. These frameworks suggest that students can opportunities presented by the challenges encountered during gameplay-based training[27]. When a task is perceived as too difficult or too easy, it can result in suboptimal learning experiences. Therefore, game-based tasks must be carefully designed to balance challenge and skill to maintain student engagement and foster a positive learning experience[28].

The role-playing game is categorized as a serious game, created specifically to teach table tennis techniques to students[29]. It is developed within an educational game design framework and consists of three levels, each providing different degrees of instructional support to assist students in shaping and developing their technical skills[30]. After each level, a challenge is introduced to ensure that players have thoroughly understood and correctly executed the techniques as demonstrated in the instructional visuals[30]. These challenges are carefully designed to balance technical difficulty and task complexity, thereby fostering students’ confidence and preventing discouragement or dropout during the learning process[31]. The overall workflow of the VR-based training games is illustrated in Fig. 1.

Fig. 1.

Overview of the workflow and components of the VR-based training games

• • Level 1: Full trajectory+racket models
• Challenge: ≥18/25 strokes accurate in ≤90s (72% threshold)
• • Level 2: Partial trajectory; speed focus
• Challenge: ≥18/25 strokes in ≤90s (increased precision)
• • Level 3: Backswing model only
• Challenge: ≥20/25 strokes in ≤90s (autonomous)
• Scoring: 1 point/stroke meeting 3 criteria (Racket trajectory path, Two Sample Racket Faces, follow through).

3-2 Game Structure and Progression

The VR-based training game consists of five sequential steps designed to guide students through the complete forehand and backhand training process, as illustrated in Fig. 2.

Fig. 2.

5 steps to guide students in correctly performing the forehand and backhand techniques

• • Posture Tutorial: Video demonstration of stance/coordination
• • Preparation: Controller based racket grip+ backswing alignment
• • Trajectory Practice: Guided swing path for muscle memory
• • Racket Face: Ball contact angle mastery via fixed model
• • Follow-through: Complete stroke completion for power/accuracy

3-3 Game Elements & Features

Game elements are essential components that support students’ object recognition and enrich their experiential perception during gameplay.

• • Points: All game scenarios utilize a common point accumulation system, which reflects the students’ performance and serves to stimulate enthusiasm and satisfaction, particularly when students achieve high scores. The goal-oriented nature of the point system encourages repeated participation and motivates students to return to practice sessions[32],[33].
• • Items: Points earned through gameplay can be used to purchase various virtual items, allowing students to customize their experience according to personal preferences, thereby supporting a greater sense of immersion[34]. Available items in the game include skins of rackets, the “spin wheel” tool, and table (As shown in Fig. 3).
• • Visual Support Tools: Visual simulation tools provide students with the ability to observe and follow proper techniques independently, without requiring direct instructor intervention[35]. These visual aids include the racket trajectory sample path, two reference racket positions at the backswing and contact points, and a virtual representation of the “spin wheels” tool.
• • Effects: Students can experience a sense of realistic interaction between the racket and ball, replicating real world tactile feedback[36]. Additionally, dynamic visual and audio effects, unrestricted by physical constraints, support enjoyment and motivation each time students correctly execute a technical movement[37],[38].

Fig. 3.

Skins of rackets, spin wheel, and table

Ⅳ. Game Development/Implementation

4-1 Interaction and Physics

1) Racket Physics Motion in a Realistic VR Experience

The velocity is applied to the racket’s Rigidbody component only when its magnitude exceeds a predefined threshold (0.01m/s), preventing unwanted jitter from minor controller movements. To further support immersion, a swing sound is triggered when the controller’s velocity exceeds a swing threshold (15.0m/s). This is implemented in the PlaySwingSound method, which uses a SoundBuilder from the audio system to play a sound with randomized pitch at the racket’s position, simulating the auditory feedback of a real table tennis swing. A cooldown mechanism (0.5seconds) prevents repetitive sound playback during continuous swings, a more natural audio experience.

2) Tactile Feedback Simulation of the Controller

The haptic feedback is triggered upon collision between the racket and the ball, utilizing the Oculus controller’s vibration capabilities.

The collision detection is handled by Unity’s physics engine, with both the racket and ball assigned appropriate colliders (e.g., sphere or capsule colliders) to ensure accurate detection. The system is designed to be performance-efficient, as it leverages Unity’s built-in physics system without requiring additional computational overhead.

3) Collision Detection between Racket and Spin Wheel

To evaluate the accuracy of the user’s stroke movement, six collider points were integrated as checkpoints. A successful execution is recorded only when the racket trajectory intersects all designated points in the correct sequence.

The colliders are used as the checkpoints for each part of the forehand and backhand techniques guideline, as shown in Fig. 4. We determine which kind of collider should be used in this situation. Ordinarily, the sphere collider is the most computationally efficient. Therefore, it was selected for this implementation to optimize performance.

Fig. 4.

Colliders used for checking forehand (left) and backhand (right) techniques guidelines

4-2 Racket Trajectory Visualization and Simulation

This study utilizes the coordinate and angular data collected from users to simulate the forehand and backhand stroke movements in response to incoming balls, based on the research conducted in [39]. The kinematic simulation was developed using Blender, adhering to the joint coordinate system and human motion modeling standards established by the International Society of Biomechanics (ISB)[40]. Fig. 5 depicts the range of rotational motion across body segments and joints. The system precisely defines the coordinates of the critical backswing and contact points, subsequently deriving the follow-through trajectory in accordance with the kinematic simulation model proposed by [5]. The simulated trajectories are illustrated in Fig. 6 and Fig. 7.

Fig. 5.

Rotational degrees of freedom of human body segments based on ISB joint coordinate system guidelines

Fig. 6.

Trajectory in a three-dimensional coordinate system for forehand and backhand techniques

Fig. 7.

Racket trajectory sample path in the game, corresponding to each forehand and backhand technique

Table tennis is a high-speed sport. Therefore, students must minimize any unnecessary movements that could reduce their responsiveness during gameplay. This highlights the importance of the reference racket’s motion trajectory in supporting the development of proper techniques. The trajectory of the reference racket should provide a clear and comprehensive visual representation for students, illustrating a continuous curve that passes through the key phases: backswing, contact, and follow-through.

4-3 Specifications of the Two Sample Racket Faces

Table tennis technique is particularly characterized by its reliance on the racket face angle during the swing and, most critically, at the moment of ball contact. The shape parameters and coordinates of the sample racket at two key positions, backswing and contact, enable students to independently understand the required racket angles, and these are simulated using the 3D modelling tool Blender. The three-dimensional data necessary for these two racket models was referenced from the study in [39] (See Table 2).

Table 2.

Angular parameters of the sample racket for both forehand and backhand techniques at two key positions: Backswing and Contact

Based on the data analyzed and synthesized in Table 2, the Blender application is used to create two sample racket faces at two critical positions: backswing and contact. Fig. 8 illustrates examples of forehand and backhand racket angle simulation in the Blender tool.

Fig. 8.

Illustration of the backswing and contact positions of the forehand and backhand techniques in blender

After the modeling process is completed, these models are transferred to the Unity tool, where they are integrated into the game environment to support visual guidance for students, as shown in Fig. 9.

Fig. 9.

The images of the two sample racket surfaces at the backswing and contact positions, as displayed in the VR-based training game, correspond to the backhand technique on the right and the forehand technique on the left

Ⅴ. User Study

Ⅵ. Results and Discussion

A total of 40 students participated in the VR activity as part of their regular table tennis course.

As shown in Table 3, students rated all dimensions of the VR experience highly, with mean scores ranging from 4.15 to 4.40. The relatively low standard deviations (SD<0.73) indicate consistent responses among participants.

Table 3.

Student feedback on VR experience(n=40)

Within the VR-based training environment, forehand performance was generally higher than backhand performance, while backhand results showed slightly greater variability, suggesting differences in task difficulty (See Table 4).

Table 4.

Technical performance results in VR and real-world environments(n=40)

As shown in Table 5, correlation analysis revealed that most user experience variables were not significantly associated with performance outcomes. However, one item (EXP4) showed a statistically significant moderate positive correlation with forehand performance (r=0.364, p=0.021). This suggests that specific aspects of user experience may be more relevant than the overall construct.

Table 5.

Pearson correlation between user experience items and performance

As shown in Table 6, regression analysis indicated that VR performance was positively associated with real-world performance for both forehand (β=0.429, p=0.001) and backhand (β=0.322, p=0.009). These results suggest a potential relationship between in-game performance and real-world skill execution, although causal interpretation is limited by the study design.

Table 6.

Regression analysis: VR performance predicting real performance

As shown in Fig. 10, VR performance is positively associated with both forehand and backhand outcomes, supporting a transfer effect. In contrast, only EXP4 shows a noticeable relationship with performance, while other user experience variables do not.

Fig. 10.

Scatter plots illustrating key relationships. (a) VR forehand performance (FH_VR) shows a strong positive relationship with real-world performance (FH_R). (b) VR backhand performance (BH_VR) also demonstrates a positive relationship with real performance (BH_R). (c) EXP4 shows a moderate positive relationship with forehand performance, while other user experience variables were not significant

Our finding that EXP4 (perceived usefulness) showed significant correlation with skill performance (r=0.364, p=0.021) aligns with Na, who identified perceived satisfaction as a key driver of VR game engagement in the Journal of Digital Contents Society, suggesting that VR-based training can leverage similar motivational mechanisms[41].

While trajectory visualization was incorporated as a feedback mechanism, its specific effect should be interpreted cautiously due to the lack of direct comparison. In addition, the integration of customizable racket skins and spin wheels was intended to enhance immersion and engagement. This design approach is supported by Tian & Woo, who reported that visually appealing game elements can increase user immersion and sustained engagement[42]. However, in the present study, these design features can not be independently evaluated, and their contribution to performance remains suggestive. Overall, the findings indicate that interaction-related factors may be more relevant than general usability in sports-specific VR-based training games.

Ⅶ. Conclusions

In conclusion, the VR-based training game demonstrates potential as a tool to support table tennis practice in a classroom setting. Positive associations were observed between in-game performance and real-world outcomes, suggesting that VR-based training may help facilitate skill practice.

While general user experience was not strongly associated with performance, specific aspects of experience may still play a role. These findings provide preliminary insights into the use of VR in sports education.

Future research should further validate these findings using randomized controlled designs with pre-post measures and more diverse participant groups (e.g., left-handed players and varied body types). In addition, detailed logging of level-specific performance (Level 1-3) should be incorporated to better evaluate progression within the stepwise training design, along with investigations into long-term skill retention.

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