Systematic review of motion capture in virtual reality: Enhancing the precision of sports training

Abstract

In the modern era of sports training, the synergy between motion capture and Virtual Reality (VR) offers an innovative approach to enhancing training precision. This systematic review delves into the application of motion capture within VR for sports training, highlighting its transformative potential. Through a comprehensive literature search, we examined the myriad applications, from physical conditioning enhancements to accelerated rehabilitation processes. Our findings underscore the capability of real-time feedback, immersive training environments, and tailored regimes that this fusion provides. However, despite its promise, challenges such as hardware constraints, data processing complexities, and interaction interface limitations persist. Future trajectories indicate an increasing influence of AI and deep learning, promising more sophisticated hardware and a broader spectrum of applications, including niche sports disciplines. The review concludes with an emphasis on the wider societal implications, suggesting a shift towards a holistic athlete well-being approach.

Keywords

virtual reality motion capture sports training immersive experience real-time feedback injury prevention

1. Introduction

Sports training, at its core, encapsulates the systematic and purposeful practice aimed at honing athletic skills and improving physical performance [31,69]. While the primary objective has always been to enhance athletic prowess, sports training holds broader implications. It plays a pivotal role in injury prevention [26], ensuring athletes adopt correct techniques and postures that reduce the risk of strains or trauma. Beyond the realm of professional sports, training has profound effects on improving general life quality [104]. Engaging in regular physical activity bolsters health, augments mental well-being, and fosters discipline [76,103]. Moreover, foundational training serves as a cornerstone, preparing individuals for more rigorous, high-intensity workouts and competitions [10]. Over the decades, sports training methodologies have undergone significant evolution. Traditional training approaches, while effective, often lacked precision and real-time feedback. Enter VR – a technological marvel that has reshaped numerous industries, including sports training. VR offers an immersive experience, plunging athletes into simulated environments that closely mimic real-world scenarios [59]. This immersive nature facilitates better spatial awareness [62], decision-making [83], and technique refinement. Furthermore, the symbiotic relationship between VR and sports training is accentuated by the real-time feedback provided through VR systems [42]. Athletes can instantly receive insights into their performance, make necessary adjustments, and consequently enhance their training efficiency.

As sports training continually strives for precision, the integration of motion capture within VR has emerged as a game-changer [14]. Motion capture, often colloquially termed as ‘mocap’, involves recording the movement of objects or people [58]. In the context of sports training, it translates to capturing the intricate nuances of an athlete’s movement, posture, and technique [65]. What makes motion capture indispensable in VR-enhanced sports training is its capability to provide immediate feedback [118]. Traditionally, athletes would rely on subsequent video reviews or coach observations to refine their techniques. With mocap integrated into VR, athletes receive instantaneous feedback on their posture and performance [100]. This real-time data allows them to make on-the-spot corrections, facilitating a more efficient and effective training session. Moreover, the granularity of data captured by mocap systems offers insights that might be imperceptible to the human eye [33]. From the angle of a tennis serve to the slight tilt in a sprinter’s posture, every minute detail is captured, analyzed, and presented to the athlete [49,85]. This level of detail fosters a deeper understanding of one’s performance, highlighting areas of excellence and pinpointing avenues for improvement. In essence, the fusion of motion capture with VR elevates sports training to unprecedented heights [77,95]. It merges the tangible realm of physical performance with the analytical prowess of technology, ensuring athletes are equipped with comprehensive insights to refine and perfect their skills.

The primary objective of this systematic review is to delve deep into the intersection of motion capture technology, virtual reality, and sports training. As technology continually reshapes the landscape of sports training, understanding its nuanced implications becomes imperative. This review aims to elucidate the transformative potential of integrating motion capture with VR, emphasizing how it enhances the precision of sports training. Our contribution to the academic and athletic communities is multifaceted. We offer a comprehensive synthesis of existing literature, bridging the gap between technology and athletic training. This review also sheds light on the current challenges and future prospects, serving as a roadmap for researchers and practitioners alike. Furthermore, by highlighting practical applications and real-world implications, this article aims to spur further innovation and exploration in the domain.

In Section 2, we delve deep into the evolution of both VR and motion capture technologies, underscoring their convergence and significance in sports training. Section 3 elucidates our meticulous approach to the literature, detailing our search strategy, study selection criteria, and data extraction methods as per the PRISMA guidelines. In Section 4, we present the tangible applications of motion capture-based VR across various facets of sports training, ranging from fitness and physical conditioning to rehabilitation and psychological training. Section 5 is dedicated to discussing the present challenges faced in integrating motion capture with VR, along with a projection of potential future trends and technological advancements. Conclusively, Section 6 encapsulates the key findings of this review, emphasizing its implications for subsequent research and real-world applications in sports training.

2. Motion capture in virtual reality

2.1. Development and key technologies of VR

VR has traversed a long journey since its conceptualization. Initially perceived as a mere digital novelty, VR has matured into an influential technological force shaping various industries, notably sports training [44]. The inception of VR can be traced back to the early 1960s with the creation of the Sensorama, a mechanical device offering a multi-sensory experience [9]. This was followed by the first HMD system, ‘The Sword of Damocles’, developed by Ivan Sutherland and Bob Sproull in 1968 [98]. With the advent of computer technology in the 1980s, VR saw significant advancements. Systems like the Virtual Environment Workstation Project by NASA paved the way for the more sophisticated VR systems we know today [57]. The 1990s and 2000s were marked by the proliferation of VR in gaming, with companies like Sega and Nintendo dabbling in VR gaming platforms. However, it wasn’t until the 2010s, with the launch of Oculus Rift, that VR truly entered the mainstream market [24]. This period marked a rapid evolution in VR hardware and software. The development of low-latency HMDs, high-resolution displays, and powerful graphics processing units (GPUs) has made VR experiences more realistic and immersive.

Beyond hardware, software innovations have played a pivotal role. Platforms like SteamVR [19], Oculus Home [41], and PlayStation VR [39] have provided a plethora of content for users. VR’s scope has expanded beyond gaming to sectors like healthcare, education, and, notably, sports training. Concurrent technological advancements, such as augmented reality (AR) and mixed reality (MR), have further broadened the horizons of immersive technologies [46,80]. Tools like Microsoft’s HoloLens combine real and virtual worlds, offering a hybrid experience [67].

2.2. Evolution of motion capture technology

The realm of motion capture has witnessed a transformative journey, evolving from rudimentary tracking techniques to the sophisticated technologies we observe today. The precision and detail these technologies offer have been instrumental in enhancing various domains, particularly sports training in the context of Virtual Reality [88,90].

The inception of motion capture revolved around basic sensor technologies. Reflective markers were placed on key points of an individual’s body, and cameras would track their movement based on the reflection of light from these markers. Magnetic field sensors [2], another early technology, gauged the movement of body parts by interpreting alterations in magnetic fields. Though revolutionary at the time, these methods had limitations, particularly in terms of accuracy and the range of motion that could be captured.

The next wave of innovation was ushered in with optical systems [121]. These systems employed multiple cameras placed at various angles to track markers on the body. The triangulation of data from these cameras resulted in more accurate and comprehensive motion capture. This method became a favorite in both the film industry and sports biomechanics due to its precision.

The introduction of depth cameras marked a significant leap [113]. Tools like Microsoft’s Kinect brought about the possibility of capturing the entire body’s motion without necessitating markers. Using infrared technology, Kinect mapped the depth and contours of objects, enabling the tracking of human movements with surprising accuracy [37]. This technology democratized motion capture, making it more accessible and straightforward.

As technology advanced, the focus shifted to capturing more nuanced movements, leading to the development of wearable devices, such as rings that track finger motion [1,120]. Gloves embedded with sensors could capture intricate hand movements, and full-body suits offered detailed data on body dynamics. These wearable devices, while offering detailed insights, also ensured that the natural movement of the user was not hampered [93,94]. For example, TSai et al. [100] explored a VR-based basketball training system enhanced with vibrotactile gloves to simulate real-world ball interactions. Although haptic feedback did not significantly impact passing reaction time, participants felt the gloves enhanced their passing and catching experience.

The latest in motion capture evolution is the fusion of data from various sources [109]. Combining data from optical systems, wearables, and depth cameras allow for a holistic view of movement. Advanced algorithms and software tools enable the synthesis of this data, providing insights that are both detailed and comprehensive.

3. Material and methods

The literature search and selection process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [61]. This ensured a comprehensive and unbiased review of motion capture in virtual reality for sports training. (Figure 1).

Figure 1.

Literature review process.

3.1. Literature search strategy

The literature search was conducted across five major databases: Scopus, Web of Science, PsycINFO, ScienceDirect, IEEE Xplore, Embase, and PubMed. The search leveraged a combination of keywords or terms such as “virtual reality”, “motion capture”, “track*”, “sports”, “fitness”, “decision making”, “basketball”, “table tennis”, “soccer”, “rowing”, “skiing”, “running”, “archery”, “team”, “collaboration”, and “rehabilitation”. Emphasis was placed on articles published from January 2018 to October 2023. This period was chosen because of the rapid advancements in both virtual reality and motion capture technologies in the last five years, rendering the insights from this timeframe particularly relevant and current.

3.2. Study selection criteria

Selected articles had to meet certain criteria to ensure relevance and quality. Firstly, only articles were considered, and they had to be published in English. Furthermore, the scope was narrowed down to those articles which were intertwined with virtual reality, motion capture technologies, and their application in sports training.

An initial search yielded 987 studies. After an initial screening, which involved removing duplicates and articles that were deemed irrelevant based on titles and abstracts, 756 studies remained. A more in-depth assessment of the full texts of these articles resulted in further refinement, with 214 articles assessed for their detailed relevance. Ultimately, 54 studies met all the criteria and were included in this systematic review.

3.3. Data extraction

We utilized the Cochrane Risk of Bias tool [5] to assess the quality of the studies incorporated in our analysis. This tool evaluates the potential bias in various aspects, encompassing reporting bias, selection bias, detection bias, attrition bias, performance bias, and other potential sources of bias. For the final set of 54 studies, key information was extracted. This encompassed aspects like the study design, sample size, and primary findings, especially those highlighting the applications in sports training. Data from these studies were qualitatively synthesized, allowing for the identification of overarching themes, potential benefits, challenges, and prospects of motion capture-based VR in sports training.

4. Practical applications of motion capture based VR in sports training

In today’s rapidly evolving digital age, the confluence of motion capture and VR has carved out novel frontiers in the domain of sports training. This powerful merger elevates the sports training landscape by offering a unique blend of precision, interactivity, and adaptability. As sports training transcends beyond mere physical prowess to a holistic amalgamation of physicality, cognition, strategy, and recovery, leveraging innovative tools becomes paramount. In this section, we unravel the multifaceted applications of motion-capture-based VR in sports training, exploring how it resonates across diverse dimensions of the athletic experience.

4.1. Fitness and physical conditioning

In the sprawling canvas of sports training, physical conditioning stands tall as a cornerstone, functioning as a linchpin that binds together an athlete’s endurance, strength, and flexibility. It’s imperative to stress the convergence of VR and motion capture in the domain of fitness and physical conditioning as it has not only revolutionized traditional methodologies but has also amplified the granularity of feedback athletes receive. Harnessing the immersive capabilities of VR combined with the precision of motion capture can shape training regimens that are tailored, responsive, and deeply immersive, offering athletes a chance to engage with their regimen in unprecedented ways. Table 1 summarizes the literature review findings in fitness and physical conditioning.

Table 1
Summary of literature review findings in fitness and physical conditioning.

References Fitness type Study design Participants VR setup Performance measure

Feodoroff et al. [30] Aerobic physical activity; muscle-strengthening activities Cross-sectional experiment 33 participants Icaros system; HMD; Heart rate monitor Muscle activity; heart rate; rate of perceived exertion

Basha et al. [8] Xbox Kinect training Randomized controlled trial 40 participants Xbox Kinect-based virtual reality system VO2peak; muscle strength; lean mass quality of life; physical activity enjoyment

Sultan et al. [97] Exergaming by Xbox Kinect Parallel double-blind randomized control trial 41 individuals Xbox Kinect Functional mobility; independence; trunk coordination extension

Vries et al. [22] Balance training Controlled trial 30 young adults; 30 healthy older adults Wii Balance board; Kinect sensor Functional limits of stability

Rezaei et al. [81] Performance-based limits of stability; curve tracking; sit-to-stand; turning Preliminary single-blind, randomized controlled trial 12 postmenopausal women Xbox 360 games; a kinect camera Center of pressure

Kantha et al. [45] VR-skateboarding and walking Biomechanical experiment 20 participants A split-belt treadmill; HMD; a skateboard Trunk flexion angles; muscle activity; hip flexion

Pourazar et al. [72] Reaction time training Randomized controlled trial 30 boys Xbox 360 Kinect Simple reaction time; discriminative reaction time

Rutkowski et al. [84] Reaction time training Pilot study 14 individuals HTC Vive Pro headset; a connected laptop Reaction time

References	Fitness type	Study design	Participants	VR setup	Performance measure
Feodoroff et al. [30]	Aerobic physical activity; muscle-strengthening activities	Cross-sectional experiment	33 participants	Icaros system; HMD; Heart rate monitor	Muscle activity; heart rate; rate of perceived exertion
Basha et al. [8]	Xbox Kinect training	Randomized controlled trial	40 participants	Xbox Kinect-based virtual reality system	VO2peak; muscle strength; lean mass quality of life; physical activity enjoyment
Sultan et al. [97]	Exergaming by Xbox Kinect	Parallel double-blind randomized control trial	41 individuals	Xbox Kinect	Functional mobility; independence; trunk coordination extension
Vries et al. [22]	Balance training	Controlled trial	30 young adults; 30 healthy older adults	Wii Balance board; Kinect sensor	Functional limits of stability
Rezaei et al. [81]	Performance-based limits of stability; curve tracking; sit-to-stand; turning	Preliminary single-blind, randomized controlled trial	12 postmenopausal women	Xbox 360 games; a kinect camera	Center of pressure
Kantha et al. [45]	VR-skateboarding and walking	Biomechanical experiment	20 participants	A split-belt treadmill; HMD; a skateboard	Trunk flexion angles; muscle activity; hip flexion
Pourazar et al. [72]	Reaction time training	Randomized controlled trial	30 boys	Xbox 360 Kinect	Simple reaction time; discriminative reaction time
Rutkowski et al. [84]	Reaction time training	Pilot study	14 individuals	HTC Vive Pro headset; a connected laptop	Reaction time

Immersive VR systems and Xbox Kinect training have shown promising results in improving aerobic activity, muscle strength, and overall quality of life, addressing challenges like cybersickness and ensuring alignment with sports science guidelines is critical. Feodoroff et al. [30] examined the effects of an immersive VR training system and found that it elicited moderate aerobic and muscle-strengthening activity in young adults, with most participants enjoying the experience (Figure 2). However, dropouts due to cybersickness and prior injuries underscore the importance of further technological enhancements and adherence to sport and exercise science guidelines in future VR training systems. In [8], the integration of Xbox Kinect training was found to considerably boost cardiopulmonary fitness, muscle robustness, lean body mass, and overall life quality. The heightened sense of pleasure reported by participants from this approach further accentuates its potential as an impactful and engaging apparatus for physical rehabilitation. In a comparative study focusing on the repercussions of Xbox Kinect VR training versus traditional workout regimes for individuals who suffered a stroke, both methodologies exhibited enhanced balance and functional autonomy [97]. Intriguingly, those in the Kinect cohort manifested pronounced improvements in functional movement, trunk synchronization, and autonomy.

Figure 2.

(A) Torso–arm angle measurement using protractor, (B) Icaros device, (C) participant being familiarized with the device, (D) upper-to-lower limb angle measurement using protractor. [30].

The efficacy of VR balance training is influenced by the type of control sensors used. Vries et al. [22] investigated the balance challenges posed by two similar skiing VR games, Wiiski and Kinski (Kinect sensor), in terms of center of mass (COM) movements relative to Functional Limits of Stability. Findings revealed that Kinski provided a more challenging balance experience than Wiiski, suggesting that the type of control sensors and their settings significantly impact the balance training efficacy of VR games. For populations with specific health concerns, like postmenopausal women with osteoporosis, VR training (VRT) equipped with body-tracking can offer superior improvements in dynamic task balance compared to conventional methods. In a study involving postmenopausal women with osteoporosis [81], virtual reality training (VRT) utilizing Xbox 360 games and a Kinect camera, which tracks the participant’s body position to provide real-time feedback, was evaluated for its effects on functional balance. The results showed that while both VRT and conventional training methods improved balance in dynamic tasks, the VRT, with its body-tracking capabilities, was notably more effective in enhancing control over weight-shifting tasks. Based on a VR-skateboarding scenario developed in Unity3D and displayed on HTC VIVE headsets (in Figure 3), participants’ biomechanics during skateboarding were compared to walking [45]. Using motion trackers on participants’ legs and skateboards, the study found that VR-skateboarding notably enhanced trunk and hip flexion, knee extensor muscle activity, and weight distribution on the supporting leg.

Figure 3.

An illustration of a virtual reality skateboarding system. [45].

Virtual reality and motion capture integration offers revolutionary improvements in physical functions, including resistance, endurance, and reaction time. Through the integration of virtual reality and motion capture technologies, Wei et al. [108] developed an algorithm to enhance the shooting techniques of basketball players. Results indicate that this method, combined with resistance training, improved shooting percentages up to 14%, with height variations being a notable factor in the outcomes. [20] underscored the potential of VR in enhancing motivation and engagement in endurance exercises for patients with chronic respiratory diseases. The “Virtual Park” system, designed for cycle-ergometer training, demonstrated promising initial results in usability and acceptability, suggesting the feasibility of integrating VR into traditional respiratory rehabilitation regimens. Using Xbox 360 Kinect as the therapeutic VR device, Pourazar et al. [72] assessed the impact of VR on reaction times in children with cerebral palsy. The Kinect’s real-time visual feedback and three-dimensional tracking capabilities facilitated significant improvements in both simple and discriminative reaction times post-intervention. The results underscore the potential of Kinect-based VR in enhancing rehabilitation for children with cerebral palsy. In [84], the VR setup tracked the movement of controllers and goggles through two sensors, and participants engaged with music tracks, slicing color-coded blocks with virtual swords in rhythm, also dodging obstacles to engage the entire body. This VR-based intervention demonstrated potential in accelerating the proficiency of young musicians in mastering instruments.

In the realm of fitness and physical conditioning, leveraging VR and motion capture offers transformative potential. However, challenges persist. Users often grapple with cybersickness, leading to higher dropout rates in VR experiences. The efficacy of balance training is influenced by sensor configurations, demanding precision in settings to achieve desired outcomes. Moreover, when targeting specific demographics, such as postmenopausal women, the need for tailored VR exercises becomes paramount. Additionally, device-specific limitations, like those observed in Kinect, may constrain their broad application.

4.2. Cognitive and mental training

At the intersection of sports and cognition lies an intricate web of decision-making, anticipation, strategy formulation, and psychological resilience [106]. While physical prowess remains paramount, the cognitive and mental faculties of an athlete often determine the outcome in high-stakes scenarios. The advent of VR and motion captute technologies has ushered in a new age of cognitive and mental training, allowing for unparalleled immersion, feedback, and adaptability. Their combination on sports training stretch beyond mere physical adaptations, diving deep into the realms of neuroplasticity, perception, and emotional regulation. Table 2 summarizes the literature review findings in cognitive and mental training.

Table 2
Summary of literature review findings in cognitive and mental training.

References Mental training type Study design Participants VR setup Performance measure

Romeas et al. [83] 3D multiple object tracking 3D-MOT trial 57 participants NeuroTracker system Speed thresholds; success rate

Tsai et al. [101] Decision-making training Between-subject experiment 45 basketball players An IMU-based full-body motion capture suit; HMD Test score; the average duration of decision time

Kittel et al. [47] Decision-making training Randomised control trial 32 Amateur Australian football umpires 360° video camera; 360°VR Psychological fidelity, engagement

Kocur et al. [48] Psychological resilience A real-world experiment 24 participants Game engine Unity3D; HTC Vive tracker Heart rate; perceived exertion; pedaling frequency; distance; body ownership; self-perceived fitness; user identification

References	Mental training type	Study design	Participants	VR setup	Performance measure
Romeas et al. [83]	3D multiple object tracking	3D-MOT trial	57 participants	NeuroTracker system	Speed thresholds; success rate
Tsai et al. [101]	Decision-making training	Between-subject experiment	45 basketball players	An IMU-based full-body motion capture suit; HMD	Test score; the average duration of decision time
Kittel et al. [47]	Decision-making training	Randomised control trial	32 Amateur Australian football umpires	360° video camera; 360°VR	Psychological fidelity, engagement
Kocur et al. [48]	Psychological resilience	A real-world experiment	24 participants	Game engine Unity3D; HTC Vive tracker	Heart rate; perceived exertion; pedaling frequency; distance; body ownership; self-perceived fitness; user identification

Figure 4.

System framework. [101].

Virtual Reality facilitates enhanced decision-making and cognitive performance in sports scenarios. Using video simulations boosts decision-making in invasion sports, but its real-world transferability and generalizability have been unclear. A study with varsity basketball players found that while both computer screen (CS) and VR training enhanced decision-making for trained plays, only VR training led to generalized improvements for untrained scenarios [66]. Delving deeper into the intricacies of VR, Romeas et al. [83] investigated a virtual training paradigm combining three-dimensional multiple object tracking (3D-MOT) with either motor or perceptual sport decision-making tasks. Results indicated dual-task training improved performance in both areas, but 3D-MOT training was more effective when sequentially done before a motor decision-making task rather than simultaneously. Meanwhile, in the basketball arena, Tsai et al. [101] proposed a VR-based basketball offensive decision-making training tool that allows intuitive user interaction via a motion capture suit, offering feedback on user decisions in varied virtual defensive setups crafted by experts. The system’s efficacy was contrasted with traditional tactics board training, and its system is as shown in Figure 4. In a contrasting sporting milieu, Australian football, Kittel et al. [47] compared the efficacy of 360° Virtual Reality (360°VR) and traditional match broadcast footage for decision-making training in amateur Australian football umpires. While the 360°VR showed notable advantages over the control group in retention tests, overall improvements were inconclusive. However, results showed that participants favored 360°VR for its enhanced psychological fidelity, engagement, and relevance over traditional footage.

Virtual Reality offers transformative experiences for athletes by manipulating stressors and perceptions, promising enhanced performance and psychological resilience. Kocur et al. [48] examined the influence of an avatar’s athleticism on users’ physiological responses and perceived effort during VR cycling exercises. Results revealed that more athletic avatars led to significant changes in users’ heart rate and their sense of exertion, suggesting potential interplays between virtual embodiment and physical exercise perceptions. Broadening the application horizon of VR beyond traditional athletic training, Liao et al. [4] examined the efficacy of VR integrated with physical and cognitive exercises to enhance dual-task gait performance and executive function in older adults with mild cognitive impairment (MCI). Using the Kinect system, the VR training adopted a range of physical activities, such as Tai Chi and functional tasks like window cleaning. Findings indicated that the VR-based approach, offering immediate visual and auditory feedback, showed marked improvements in executive function and dual-task gait performance compared to traditional combined training methods.

In the arena of cognitive and mental training, the integration of VR and motion capture technologies has been instrumental in amplifying decision-making, anticipation, and psychological resilience of athletes. The immersive environments of VR, combined with the real-time feedback of motion capture, offer athletes a nuanced training platform. Studies have indicated that while video simulations can enhance decision-making in sports, the immersive nature of VR ensures a broader application, including untrained scenarios. Furthermore, VR’s potential in manipulating athletes’ perceptions, such as using athletic avatars, can influence physiological responses and perceived effort levels, hinting at a profound link between virtual embodiment and physical training. Additionally, VR’s application isn’t limited to elite athletes. Its use in improving cognitive functions in older adults, by integrating physical and cognitive exercises, showcases its potential in a broader spectrum of applications. However, despite these advancements, ensuring the transferability of skills from VR to real-world scenarios remains a challenge.

4.3. Sport-specific training scenarios

Each sport offers its unique challenges, intricacies, and subtleties that require specialized training tools and techniques [79,92]. Modern technology, especially the seamless integration of motion capture with VR, is poised to redefine the training landscape across different sports. Table 3 summarizes the literature review findings in sport-specific training scenarios.

Table 3
Summary of literature review findings in sport-specific training scenarios.

References Sports type Study design Participants VR setup Performance measure

Liu et al. [52] Basketball training Test experiment 10 personnel Kinect sensors Accuracy; recall; response time

Soltani et al. [89] Basketball training Pilot study 12 experienced and 10 novice basketball players A realistic virtual basketball court; stereoscopic glasses Success-rate; ball and body kinematics

Shen [85] Table tennis training Pilot study 20 secondary players and 20 outstanding players Kinect2.0 motion capture equipment Euler distance; degree of participation

Chung et al. [18] Soccer training Case study A trial user A wearable motion capture device with 24 nodes; Vive’s HMD Kinect energy

Wood et al. [110] Soccer training Randomised control trial 17 professional soccer players, 17 academy players, 17 novice players A HTC Vive Pro head-mounted display; 4 detachable HTC Tracker 2.0 sensors Passing accuracy, composure, reaction time, and adaptability

Arndt et al. [6] Rowing training Pilot study 16 participants Augletics Eight2; a HTC Vive headset Stroke length; recovery; rhythm; consistency; movement

Wu et al. [111] Skiing training Controlled experiment 81 participants VR ski training simulator Ankle rotation

Ono et al. [64] Skiing training Pilot study 16 participants HTC Vive Pro; Pro Ski-Simulator Proficiency level

Perrin et al. [70] Running training Randomised control trial 17 healthy individuals HMD devices; a handheld Vive controller Error between actual and perceived locomotor speed

Purnomo et al. [74] Archery training Pilot study 10 participants Oculus Rift S Navigation, interaction, application process and satisfaction

References	Sports type	Study design	Participants	VR setup	Performance measure
Liu et al. [52]	Basketball training	Test experiment	10 personnel	Kinect sensors	Accuracy; recall; response time
Soltani et al. [89]	Basketball training	Pilot study	12 experienced and 10 novice basketball players	A realistic virtual basketball court; stereoscopic glasses	Success-rate; ball and body kinematics
Shen [85]	Table tennis training	Pilot study	20 secondary players and 20 outstanding players	Kinect2.0 motion capture equipment	Euler distance; degree of participation
Chung et al. [18]	Soccer training	Case study	A trial user	A wearable motion capture device with 24 nodes; Vive’s HMD	Kinect energy
Wood et al. [110]	Soccer training	Randomised control trial	17 professional soccer players, 17 academy players, 17 novice players	A HTC Vive Pro head-mounted display; 4 detachable HTC Tracker 2.0 sensors	Passing accuracy, composure, reaction time, and adaptability
Arndt et al. [6]	Rowing training	Pilot study	16 participants	Augletics Eight2; a HTC Vive headset	Stroke length; recovery; rhythm; consistency; movement
Wu et al. [111]	Skiing training	Controlled experiment	81 participants	VR ski training simulator	Ankle rotation
Ono et al. [64]	Skiing training	Pilot study	16 participants	HTC Vive Pro; Pro Ski-Simulator	Proficiency level
Perrin et al. [70]	Running training	Randomised control trial	17 healthy individuals	HMD devices; a handheld Vive controller	Error between actual and perceived locomotor speed
Purnomo et al. [74]	Archery training	Pilot study	10 participants	Oculus Rift S	Navigation, interaction, application process and satisfaction

Basketball, a game requiring accuracy, footwork, and coordination, has witnessed advancements in training methodologies, among which the precision offered by motion capture in VR is invaluable. This year, Liu et al. [52] presented an AR/VR-based motion capture approach to enhance college basketball training. By integrating skeletal data and employing the LSTM algorithm, the method achieved recognition rates of 85% for “shooting” and “defense” actions, and over 93% for other movements. With motion capture equipment, the action recognition time was reduced to about 210 ms, a 100 ms improvement over conventional tools. In a study examining basketball shooting in a virtual environment (Figure 5), success rates and kinematics were tracked across different scales to assess player expertise and perceptual awareness of basket distance [89]. While success rates and ball kinematics reflected expertise and distance manipulation, body kinematics only indicated player expertise and gender, underscoring the nuances of using VR for sports training.

Figure 5.

Arrangement of the hardware components of the basketball throwing simulator installed in a 6 m long × 5 m high room. [89].

Harnessing motion recognition technology, modern solutions are reshaping the landscape of sport-specific training, with table tennis serving as a pertinent example. For table tennis beginners in China, proper technical guidance is often not available, leading to incorrect techniques and potential injuries. Addressing the limitations of traditional teaching methods, Han [40] introduced an intelligent system that uses motion recognition technology, specifically through inertial sensors at skeletal points, to recognize and classify human table tennis movements. The proposed system not only aids in motion correction but also proves valuable for technical analysis and tactical planning in the sport. Similarly, in response to the push for enhanced physical education in schools, Shen [85] introduced a table tennis technical action evaluation system developed using mixed motion capture technology, specifically leveraging Microsoft Kinect2.0. This system facilitates technical guidance and evaluation for table tennis trainers, enhancing self-learning and technique assessment. The use of Kinect2.0 ensures efficient data capture and processing, making it practical and feasible for widespread adoption, ultimately advancing the integration of sports with computer technology.

Modern advancements in immersive VR gaming also spotlight the innovative use of Head Mounted Displays (HMD) and motion capture methods for soccer training. Chung et al. [18] highlighted the rise of Head Mounted Displays (HMD) for immersive virtual reality gaming. While many interaction devices, like Leap Motion and marker-based devices, have their constraints, this research introduced a wearable motion capture method, which tracked movement despite obstructions, and tested its application in a VR soccer game – marking a pioneering effort in the field. Wood et al. [110] assessed the construct validity of a soccer-centric VR simulator, MiHiepa Sports Rezzil, by testing its ability to differentiate skill levels among professional, academy, and novice players. The MiHiepa Sports Rezzil VR platform utilizes the HTC Vive Pro, a high-resolution head-mounted display, complemented by HTC Tracker 2.0 sensors affixed to players’ shoes and shin guards (Figure 6). These trackers, synchronized with the HTC Lighthouse 2.0 system, enable precise motion capture, providing a detailed and responsive simulation experience for users during soccer training drills.

Figure 6.

Showing the sensors placed on the shin guards and feet of the players (left), and the accuracy (center) and speed (right) calibration drills. [110].

In rowing, where rhythm and technique are paramount, VR combined with sensor feedback offers a novel approach to training. In a pilot study exploring the integration of VR with sensor feedback, athletes used a stationary rowing machine within a VR environment, with the machine’s sensors providing real-time movement data to the VR display [6]. Initial findings suggest that this VR setup not only enhanced the athletes’ training experience but also positively impacted their rowing performance compared to traditional training methods. To facilitate athlete training and offer non-athletes a gamified rowing experience, Shoib et al. [87] focused on creating a VR rowing simulation. By integrating the rower machine with trackers, the simulation captures real-world rowing motions, where one ergometer pull equates to a single stroke in the game, allowing for realistic speed and distance replication. At the same year, In [102], a novel interactive system was developed over three design iterations that pairs the dynamic ergometer (RP3) with the HTC Vive platform, augmented with three location trackers. This system offers enhanced opportunities for learning and refining rowing techniques, potentially reducing injury risks associated with the learning curve.

In ski training, virtual reality’s integration has proven transformative, enabling a more nuanced and feedback-driven approach to mastering the sport’s nuances. A VR-based ski training platform was developed, using an indoor simulator enhanced by two trackers to replicate ski movements on a virtual slope [111]. By studying the efficacy of visual cues and feedback, the system offers insights into leveraging pro-skier motion patterns to bolster ski training, shedding light on the potential and constraints of such VR ski training tools. Based on the above research, Ono et al. [64] introduced a VR support system to train novice skiers, emphasizing weight-shifting techniques based on prior findings from deep learning analyses. Through real-time feedback on users’ weight-shifting patterns, the system has been shown to significantly aid participants in mastering this crucial skiing skill.

In the field running and walking, the calibration of perceived motion in virtual reality remains a crucial consideration. Using an enactive approach with the HTC Vive system, Perrin and his colleagues [70] investigated the accuracy of perceived locomotion speed in virtual environments (VE) during treadmill activities. While the overall average showed accurate speed perception, individual discrepancies varied, with some consistently overestimating or underestimating. Consequently, the paper suggests personalized adjustments instead of general correction for VR locomotion applications to ensure authentic speed perception. Krasnyanskiy et al. [49] explored crafting a control system for a running platform in virtual reality, aiming for adaptive speed adjustments based on user behavior to heighten immersion and comfort. Leveraging a unidirectional treadmill and VR trackers, new control functions were devised, tested, and assessed, which minimized users’ deviations from a starting position, mitigating oscillation and inertia impacts.

From archery to first-person shooters and even Para-Badminton, the integration of motion capture and VR offers unparalleled realism and potential therapeutic applications. Aprial et al. [3] delved into the integration of motion capture with VR in creating a more immersive archery game experience. By harnessing the capabilities of both technologies, they attempted to enhance realism in archery games, positioning them not only as entertainment but also as potential cognitive development tools. On the basis of above study, a VR simulator tailored for conventional archery training was invented, leveraging the Oculus Rift S to mimic authentic archery movements [74]. The simulator’s effectiveness was evaluated based on navigation, interaction, application processes, and user satisfaction. Apart from that, an innovative system for heightened realism in VR first-person shooter games was proposed, utilizing motion controllers to track player’s hand and head movements [50]. By establishing a seamless correspondence between the physical and virtual realms, they offered refined player-gun interactions (Figure 7), verified using a VR FPS demo and a gun template, demonstrating its potential applicability to other VR motion-controlled games. As far as Para-Badminton matches, researchers from the University of São Paulo, in collaboration with the State University of Campinas, created a nocel virtual reality game [32]. Designed to boost performance and mitigate neuropathic pain in athletes with Spinal Cord Injury, this endeavor leverages the Unity 3D game engine and Leap Motion hardware.

Figure 7.

Proposed gun interface solutions, consisting of three mesh objects and an additional attachment: (a) pistol interface, (b) machine-gun interface, and (c) correspondence of the motion controller with a virtual gun interface. [50].

To sum up, the amalgamation of VR with motion capture and sensor technologies is ushering in a new epoch in sports training. It promises more effective training methodologies, personalized feedback mechanisms, and realistic simulations, potentially reshaping the future of sports training across various domains. The ensuing sections will further delve into the underlying technological innovations driving these advancements and their broader implications for athletes and trainers alike.

4.4. Team collaboration and strategy

In the domain of team sports and collaborative endeavors, VR has come to the forefront as a versatile tool for assessing and training players. Table 4 summarizes the literature review findings in team collaboration and strategy.

Table 4
Summary of literature review findings in team collaboration and strategy.

References Team collaboration Study design Participants VR setup Performance measure

Vu et al. [105] Multiple soccer players training Pilot trials 32 volunteers HTC VIVE EYE PRO Visual tracking performance; gaze activity; search rate

Fan et al. [27] Team training Randomised experiment 6 participants HMD; VoIP headset Simulated training performance

Mas et al. [56] Spatial navigation training Proof of concept N/A Indy, a virtual reality system N/A

Gugenheimer et al. [36] Co-located social interactions Exploratory user study 16 participants FaceDisplay, a mobile VR HMD Enjoyment; social interaction; presence; emotional state

Chen et al. [16] Collaborative VR for learning Pilot study 30 undergraduate students Oculus CV1 HMD; Oculus Touch Task performance; engagement level; collaboration patterns

References	Team collaboration	Study design	Participants	VR setup	Performance measure
Vu et al. [105]	Multiple soccer players training	Pilot trials	32 volunteers	HTC VIVE EYE PRO	Visual tracking performance; gaze activity; search rate
Fan et al. [27]	Team training	Randomised experiment	6 participants	HMD; VoIP headset	Simulated training performance
Mas et al. [56]	Spatial navigation training	Proof of concept	N/A	Indy, a virtual reality system	N/A
Gugenheimer et al. [36]	Co-located social interactions	Exploratory user study	16 participants	FaceDisplay, a mobile VR HMD	Enjoyment; social interaction; presence; emotional state
Chen et al. [16]	Collaborative VR for learning	Pilot study	30 undergraduate students	Oculus CV1 HMD; Oculus Touch	Task performance; engagement level; collaboration patterns

Vu et al. [105] utilized Virtual Reality to investigate the visual tracking capabilities of soccer players compared to non-players in tracking moving virtual characters. While soccer players exhibited superior tracking abilities, their expertise did not provide an advantage in scenarios mimicking real game trajectories. Bonfert et al. [27] emphasized the integration of body area networks (BANs) with VR to enhance soldier-based team training, compensating for spatial constraints in traditional facilities. Experiments, as indicated by the tracking of arm movements in a T-pose, demonstrate the system’s proficiency in directly monitoring motion trajectories.

To enhance spatial navigation skills essential for complex industrial settings, Mas et al. [56] introduced “Indy,” a virtual reality-based collaborative treasure hunting game. Similarly, Wu et al. [112] developed “Sky Classroom,” a global project-based course emphasizing collaborative building design. This avatar-driven tool, evolving from desktop to immersive virtual reality versions, enhances collaboration by immersing users within the BIM model.

The momentum of VR applications in team collaboration and strategy has spilled over into engineering and industrial sectors as well. To streamline lifecycle engineering tasks, a multi-user VR training system was proposed, meticulously integrated with virtual factories, focusing on wind turbine assembly [115]. Preliminary evaluations by experts highlighted the system’s potential to revolutionize industrial training effectiveness and efficiency. “FaceDisplay” [36] is an innovative VR headset embedded with a depth camera and touch-sensitive screens, allowing bystanders to view and interact with the virtual realm experienced by the primary user. Initial applications and user studies suggested potential for enhancing co-located social interactions, challenging the prevailing HMD-centric design approach to be more inclusive of non-HMD users. As far as collaborative VR for learning, it was found that a shared view improved task performance and that side-by-side positioning optimized user experience [16]. Notably, users’ movements were realistically mirrored in the VR space, as their heads and hands were tracked in 6 DoF by the Oculus Touch sensors, enabling accurate representation through avatars. To cope with the limitations of traditional VR systems, Ha et al. [38] developed a motion-capture-based VR collaboration tool, enhancing immersion by aligning users’ real movements with their virtual avatars. The system adjusted the avatar’s size for user-body congruence, with experiments indicating minimal height discrepancies, and successful remote collaboration trials involving multiple participants (Figure 8).

Figure 8.

Multi-remote collaboration test. [38].

VR is revolutionizing team sports and collaborative strategies, offering nuanced platforms for enhanced training. From evaluating soccer players’ visual tracking to military spatial training, VR’s scope is vast. The industrial sector, too, is embracing VR for spatial navigation and collaborative design. Innovations like “FaceDisplay” allow bystanders to participate in a user’s virtual experience, broadening VR’s inclusivity. As VR integrates with motion capture, it promises to reshape team collaboration across diverse fields.

4.5. Rehabilitation and injury prevention

As sports training continually seeks precision and optimization, there arises a profound need to safeguard the well-being of athletes and individuals who engage in physical activities. This safeguarding is twofold: ensuring efficient rehabilitation post-injury and preventing future occurrences. As injuries can range from minor strains to debilitating conditions, a holistic approach to recovery is essential. Not only does VR offer a dynamic and interactive platform that enhances patient engagement, but the precision offered by motion capture provides invaluable data that can be harnessed to tailor specific therapeutic interventions. Table 5 summarizes the literature review findings in rehabilitation and injury prevention.

Table 5
Summary of literature review findings in rehabilitation and injury prevention.

References Rehabilitation type Study design Participants VR setup Performance measure

Mihajlovic et al. Head-neck rehabilitation exercises A user study 30 users aged 18–50 Oculus Rift DK2 VR headset Mean tracking scor

Bortone et al. Rehabilitation of upper limbs Controlled trial 20 subjects Two dedicated immersive SG and a Graphical User Interface (GUI) Kinematic measurements

Prasad et al. Rehabilitation of upper limbs Pilot randomized controlled trial 22 patients Nintendo Wii Capabilities of upper extremity

Fang et al. Sports rehabilitation Pilot study 40 students in the first class and 45 in the second class HMD Excellence rate of the experimental class

Askin et al. Upper extremity motor functions Randomized controlled trial 40 chronic stroke patients Xbox Kinect Upper extremity (UE) Fugl-Meyer Assessment (FMA)

Shen et al. Physical rehabilitation Pilot study 10 healthy children and 4 children with TBIs An HTC Vive VR headset and Vive controller Engagement; user experience

Choi et al. Upper-limb function rehabilitation Randomized controlled trial 80 children with brain injury Virtual reality rehabilitation system Functional and kinematic assessments

References	Rehabilitation type	Study design	Participants	VR setup	Performance measure
Mihajlovic et al.	Head-neck rehabilitation exercises	A user study	30 users aged 18–50	Oculus Rift DK2 VR headset	Mean tracking scor
Bortone et al.	Rehabilitation of upper limbs	Controlled trial	20 subjects	Two dedicated immersive SG and a Graphical User Interface (GUI)	Kinematic measurements
Prasad et al.	Rehabilitation of upper limbs	Pilot randomized controlled trial	22 patients	Nintendo Wii	Capabilities of upper extremity
Fang et al.	Sports rehabilitation	Pilot study	40 students in the first class and 45 in the second class	HMD	Excellence rate of the experimental class
Askin et al.	Upper extremity motor functions	Randomized controlled trial	40 chronic stroke patients	Xbox Kinect	Upper extremity (UE) Fugl-Meyer Assessment (FMA)
Shen et al.	Physical rehabilitation	Pilot study	10 healthy children and 4 children with TBIs	An HTC Vive VR headset and Vive controller	Engagement; user experience
Choi et al.	Upper-limb function rehabilitation	Randomized controlled trial	80 children with brain injury	Virtual reality rehabilitation system	Functional and kinematic assessments

VR has emerged as a groundbreaking tool in the realm of rehabilitation and injury prevention, offering tailored solutions to a spectrum of physical challenges, such as neck pain, shoulder rehabilitation, upper limb rehabilitation, and lower limb rehabilitation. In [60], a VR system was proposed, which utilized the Oculus Rift DK2 headset to motivate individuals with neck pain to adhere to prescribed exercises. By immersing users in a tailored exergame designed around their specific neck range of motion, the system not only promotes rehabilitative movement but also assesses neck flexibility. In shoulder rehabilitation monitoring, the Oculus Quest 2 [15] demonstrated a mean absolute error of 13.52 ± 6.57 mm for translational displacements at 500 mm from the head display in the x-direction, and a maximum error of 1.11 ± 0.37° for 40° rotational movements around the z-axis. Given these results, the Oculus Quest 2 offers promise as an effective alternative to conventional motion tracking systems in rehabilitation scenarios. Bortone et al. [12] presented an immersive VR rehabilitation system with wearable haptics tailored for children with neuromotor challenges, as shown in Figure 9. Initial trials with children affected by cerebral palsy and developmental dyspraxia indicate the system’s adaptability to varying motor skills and its potential as a tool for kinematic assessment of motor functions. In [73], the effects of incorporating Nintendo Wii-based VR with conventional therapy were compared with conventional therapy on upper limb function in spinal cord injury patients, results after 4 weeks revealed comparable improvements in hand function for both groups. VR offers an engaging and interactive dimension to traditional rehabilitation, serving as a form of biofeedback and allowing patients to monitor daily performance progress. Zhang et al. [117] employed wearable TENG-based devices for detailed gait and waist motion analysis, aiding in lower-limb and waist rehabilitation, as shown in Figure 10. By integrating these devices with virtual gaming, they enhanced immersion during rehabilitation sessions.

Leveraging virtual scene position mapping focused on upper limb movements [28], a study on sports rehabilitation students showed significant improvements in practical test scores, with the experimental group outperforming the control by 24.2% in excellence and 12% in pass rates, suggesting that VR applications not only enhance practical skills but also pique learners’ interest, with motor skills in virtual setups being transferable to real-world scenarios. Yan [114] delved into using virtual reality for sports rehabilitation, introducing an AR algorithm for dynamic target tracking in VSLAM and leveraging OpenPose for hand gesture recognition in patient training. Through Unity3D and Photon Server, a multi-user virtual training environment was created, notably improving tracking accuracy in areas like head positioning and leg movement based on users’ perspectives in a motion capture system.

Figure 9.

Overview of the proposed rehabilitation system with a close-up of the VE visualized through the HMD (Head Mounted Display), and of the two wearable haptic devices rendering contact forces. [12].

Figure 10.

Schematics of the AIoT-based smart healthcare. [117].

Moreover, evidence from a randomized trial underscored the advantages of amalgamating Kinect-based VR training with traditional physical therapy for chronic stroke patients [7]. The synergistic approach led to improvements in upper extremity motor functions and range of motion compared to exclusive physical therapy treatments. To elevate the motivation levels of stroke patients and make repetitive exercises less tedious, Dias et al. [25] introduced VR mini-games tailored for upper limb exercises. Though some challenges were encountered, such as sensor placement and game misalignment, these were addressed through methodical adjustments, underscoring the adaptability of VR in rehabilitation scenarios.

The growing integration of VR into cognitive rehabilitation demonstrates its promising potential, especially in children with traumatic brain injuries (TBI) and associated motor challenges. In 2020, a VR system tailored for cognitive rehabilitation was devised, targeting three core executive functions, in children with traumatic brain injuries [86]. While prior VR tools focused on physical rehabilitation, this innovative system, well-received in pilot testing, emphasizes cognitive recovery post-TBI in children. In a case study involving a young male with severe TBI, the Computer Assisted Rehabilitation Environment (CAREN) system – a virtual reality tool by MOTEK Medical featuring motion capture and a multi-faceted VR immersion experience – was employed alongside standard cognitive rehabilitation [21]. Notably, significant cognitive and motor improvements were observed only post-CAREN training, suggesting the potential efficacy of immersive VR in TBI cognitive rehabilitation. Choi et al. [17] explored the effectiveness of a wearable sensor-based virtual reality system in enhancing upper-limb function in children with brain injuries (Figrue 11). Results revealed that the VR group, which incorporated repetitive task-oriented games using wearable inertial sensors, showcased notably better upper-limb dexterity and daily activity performance than the conventional therapy group.

Figure 11.

Component of the virtual reality device developed for upper-limb rehabilitation in children with disabilities. [17].

In rehabilitation and injury prevention, the blend of VR and motion capture offers personalized recovery pathways. While VR enhances engagement and offers tailored exercises, challenges persist in seamlessly integrating it with traditional methods. Especially in cognitive rehabilitation post-traumatic injuries, ensuring real-world skill transfer and long-term efficacy from VR training remains pivotal. The synergy between VR and conventional approaches is crucial for optimal outcomes.

5. Challenges and future trends

5.1. Current challenges

The integration of motion capture within VR for sports training brings forth a new horizon of possibilities. However, it is not without its set of challenges that need to be addressed to harness its full potential.

The size and comfort of motion capture sensors remain a challenge, especially in sports that demand unrestrained movement [23]. In sports like long-jump, these sensors can potentially shift during performance, altering the data’s accuracy. The durability of these sensors is crucial, especially when considering the vigorous movements in sports like boxing or martial arts. In terms of data processing, sports like gymnastics or ballet, where precision is paramount, require real-time, detailed feedback. This demands robust algorithms capable of instantaneous processing [51]. In sports that require immediate response, like fencing or tennis, latency can compromise the training’s effectiveness.

A single motion capture session can generate gigabytes of data, especially when capturing detailed movements. This data volume exponentially increases in VR scenarios where environments are richly detailed and interactive. The transformation of raw sensor data into meaningful feedback requires complex algorithms [35]. These algorithms need to factor in individual athlete biomechanics, the sport’s specific requirements, and potential environmental variables [43]. In sports training, a delay of even a few milliseconds in feedback can be the difference between a successful training session and a missed opportunity [29]. Ensuring that data processing and feedback provision happen in real-time, without noticeable latency, is a significant challenge.

Traditional VR interaction mechanisms, like hand controllers, may not always be feasible in high-intensity sports training [13]. Integrating voice commands [71], gaze tracking [68], or gesture recognition [34] can offer more natural interaction methods. However, ensuring their accuracy in dynamic training environments is challenging. Beyond interaction, how feedback is relayed to the athlete in VR is crucial. Visual or auditory cues need to be intuitive and non-distracting. They should enhance the training experience rather than disrupt the athlete’s focus. Athletes come from diverse training backgrounds and have varied familiarity levels with VR. The interaction interface should be customizable to cater to beginners and advanced users alike, ensuring a seamless transition into VR-enhanced training [63].

5.2. Future trends

The dynamic landscape of motion capture in VR for sports training is on the cusp of revolutionary advancements. As we look ahead, several trends emerge, promising to reshape and refine the domain further [78,91].

From a technical standpoint, the integration of Artificial Intelligence (AI) and deep learning promises to redefine the boundaries of motion capture in VR [53,55,107,119]. These technologies will not only enhance data analysis but will also provide predictive insights, enabling athletes to make preemptive adjustments. Additionally, as hardware components become more sophisticated, we can anticipate a shift towards a more seamless and integrated experience. Athletes will benefit from miniaturized sensors [54], longer battery life [99], and lighter VR headsets. Furthermore, the convergence of Augmented Reality (AR) with VR, culminating in Mixed Reality (MR), will introduce multi-dimensional training experiences, blending real-time feedback with immersive environments [96].

In terms of application, the future beckons a shift towards more adaptive training regimes [116]. These programs, driven by real-time data and analytics, will constantly evolve, adjusting to an athlete’s performance and ensuring optimal skill development. As the technology becomes more accessible and versatile, its reach will extend to specialized sports disciplines, including ice skating and swimming, ensuring a broader spectrum of athletes benefit from advanced training tools [75].

However, with these advancements come societal implications. As motion capture in VR becomes more prevalent, we can expect its adoption to spread beyond elite athletes, reaching local gyms, schools, and rehabilitation centers. This democratization of advanced training tools will inevitably raise concerns about data privacy and security [11]. Safeguarding athletes’ biomechanical data will be paramount. Moreover, as the lines between motion capture data and health metrics blur, there will be a synergistic relationship between sports training, medicine, and overall health monitoring [82]. This integration will play a pivotal role in injury prevention, recovery optimization, and holistic athlete well-being.

6. Conclusion

In the rapidly evolving world of sports training, the intersection of motion capture and VR stands out as a beacon of transformative potential. This systematic review embarked on a journey to explore this convergence, aiming to provide clarity on its implications and future trajectory.

Our study was rooted in the backdrop of a rising demand for precision and personalization in sports training. The primary objective was to elucidate the transformative potential of integrating motion capture with VR, emphasizing its capacity to redefine training methodologies. Through a rigorous examination of existing literature, we unearthed the multifaceted applications of this fusion, from enhancing physical conditioning to facilitating accelerated rehabilitation. Our findings underscored the undeniable benefits of integrating motion capture with VR, most notably, its capacity to offer real-time feedback, immersive training environments, and tailored training regimes. However, this potential doesn’t come without challenges. From hardware limitations to data privacy concerns, the road to seamless integration is riddled with obstacles. But, as with any nascent technology, these challenges pave the way for innovation.

Looking ahead, the future of motion capture in VR sports training is brimming with promise. As AI and deep learning find their footing in this domain, and as hardware becomes more sophisticated, we can anticipate a revolution in sports training. Furthermore, the societal implications of this technology’s wider adoption, coupled with its convergence with health metrics, signal a shift towards a more holistic approach to athlete well-being.

Footnotes

Funding

This review is supported by Sports Research Project of Henan Provincial Sports Bureau under Grant No. 202327.

Conflict of interest

The authors declare there is no conflict of interest.

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