Search Results (97)

Background: Upper-limb strength characteristics are considered important determinants of shooting stability in precision sports; however, the specific relationships between upper-limb strength variables and shooting performance in elite air pistol athletes remain insufficiently understood. Therefore, this study aimed to investigate the associations between upper-limb-specific strength characteristics and shooting performance in elite air pistol shooters. Methods: A prospective observational cohort study was conducted using a purposive total population sample from an elite training camp. Isometric peak force and rate of force development of nine upper-limb muscle actions, including handgrip, elbow flexion and extension, and shoulder joint movements, were assessed using a Vald Dynamo handheld dynamometer. Official scores from an international selection competition were used as indicators of shooting performance. Ridge regression analysis was applied to examine the relationships between strength variables and shooting performance while addressing multicollinearity among predictors. Results: Twenty-four elite air pistol athletes at national master level or above were recruited. Ridge regression revealed distinct coefficient patterns between upper-limb task-specific strength characteristics and total shooting score. After within-sex standardization of strength predictors, larger positive ridge coefficients were observed for handgrip RFD, elbow flexion peak force, shoulder external rotation RFD, elbow extension peak force, and selected shoulder variables, whereas negative coefficients were observed for shoulder internal rotation RFD, handgrip peak force, shoulder extension RFD, elbow extension RFD, and selected shoulder variables. These findings suggest that shooting performance is associated with the balance and coordination of task-specific upper-limb strength characteristics rather than maximal strength alone. Conclusions: These findings suggest that coordinated upper-limb task-specific strength balance is associated with shooting performance in elite air pistol athletes. These findings may help inform individualized conditioning and monitoring strategies; however, longitudinal intervention studies are needed to determine whether modifying upper-limb strength balance can improve shooting outcomes. Full article

Background: Rifle shooting performance depends on precise postural control, rifle stability, and coordinated upper-limb force production. Although previous studies have examined postural balance and aiming stability in rifle shooters, the role of upper-limb inter-limb strength asymmetry across different shooting positions remains unclear. This study explored the association between joint-specific upper-limb strength asymmetry and rifle shooting performance in elite athletes across prone, kneeling, and standing positions. Methods: Thirteen elite rifle shooters completed a 20-shot series in each position according to ISSF rules. Bilateral maximal isokinetic strength of the wrist, elbow, and shoulder was assessed at 60°/s using a Biodex System 4 dynamometer, and handgrip strength was measured with a handheld dynamometer. Inter-limb asymmetry indices were calculated for each joint action. Position-specific shooting scores and good-10 hits (≥10.0) were recorded. Associations between asymmetry indices and performance outcomes were examined using Spearman correlation analyses and simple linear regression models. Results: In exploratory analyses, greater asymmetry in selected shoulder actions showed large negative associations with lower prone, kneeling, and standing scores, and standing performance also showed a negative association with wrist flexion asymmetry. Good-10 hits were negatively associated with selected shoulder and wrist asymmetry indices. Exploratory regression models showed large apparent proportions of explained variance for selected position-specific associations, but these estimates should be interpreted cautiously because of the small sample size and the absence of validation. Conclusions: Position-specific upper-limb strength asymmetry, particularly at the shoulder and wrist, was associated with rifle shooting performance and may represent a relevant consideration for training and monitoring in elite rifle athletes. Full article

Auditory neurofeedback training (NFT) based on brain–computer interfaces (BCIs) has recently entered the precision motor domain as a task-embedded neural state regulation paradigm. Compared to traditional standalone NFT approaches (e.g., relaxation or attention training designed to enhance general cognitive abilities), task-embedded paradigms integrate feedback directly into the motor task execution process. However, this design inevitably creates a dual-task scenario, and the effects of such a scenario on neural activity and behavioral performance have received limited systematic investigation in the existing literature. This study designed and implemented a closed-loop BCI system employing five-level heartbeat sound feedback and used this system as a research platform to examine the immediate neural mechanism changes and potential dual-task interference effects induced by single-session auditory NFT in moderately skilled shooters. The system maps real-time EEG features onto graded auditory signals varying in playback rate and volume intensity, incorporating a dynamic threshold adjustment mechanism. Twenty-two moderately skilled shooters completed three within-subject conditions (no-sound baseline, SMR enhancement, and theta suppression) in a single session with 32-channel EEG and behavioral data recorded simultaneously. Analyses employed whole-brain cluster-based permutation tests, cross-frequency coupling analysis, and functional connectivity analysis. Cluster-based permutation tests revealed that theta feedback induced a significant frontal 4–7 Hz suppression cluster (cluster p = 0.004), whereas SMR feedback did not produce significant 12–15 Hz enhancement at the group level. Theta feedback elicited cross-frequency spillover as follows: sensorimotor SMR power decreased significantly in theta responders (d = −0.69), with frontal theta and sensorimotor SMR changes positively correlated (r = 0.67, p < 0.001). Functional connectivity analysis using debiased weighted phase lag index (dwPLI) further demonstrated significant theta-band network reorganization (cluster p = 0.034). At the neural level, clear modulation effects were observed, but shooting ring values did not improve significantly under feedback conditions, and aiming time was significantly prolonged—a behavioral pattern consistent with potential dual-task interference from task-embedded auditory feedback. Single-session auditory NFT can act on the prefrontal cognitive control network and induce cross-frequency network reorganization, but the feedback channel itself constitutes a parallel task that may limit the short-term transfer of induced neural states to behavioral performance. This study examined the neural mechanisms of task-embedded auditory NFT and reported the dual-task costs that have been less characterized in prior “task + feedback” research, providing design considerations and preliminary mechanistic evidence for future development of auditory NFT in precision motor skill training. Full article

Competitive eSports impose substantial cognitive workload, yet performance evaluation still emphasizes post-match statistics without considering players’ cognitive states. We reviewed 30 papers that recorded physiological signals using sensors and utilized machine learning (ML) for predicting cognitive states and/or game performance. Findings showed that cardiovascular monitoring (heart rate variability/HRV) was the most prevalent modality (20/30 studies), followed by oculometry (10), electrodermal activity/EDA (9), and electroencephalogram/EEG (5); however, no standardized protocols (device/pre-processing/feature subset) were observed across HRV studies despite it being the most common measure. The best outcomes per construct (measure, accuracy) were: mental workload (pupillometry, ~82%), stress/arousal (EDA, p < 0.001), cognitive fatigue (pupil diameter/EEG, ~88%), expertise (EEG, ~92%), and tilt (EDA/HRV/eye-tracking, ~82–87%). Notably, current studies used small samples and were gender-imbalanced, while ML studies often lacked cross-validation. Only 2 of 30 studies examined flow state—a mental state of optimal performance characterized by total immersion and effortless execution—and interestingly, HRV showed decreases during stress/workload but increases during flow, suggesting context-dependent autonomic regulation. To address this gap, a new framework for flow detection is presented. This review will be of interest to game developers, eSports players, and coaches, and the reported findings may help towards improving player experience and game performance. Full article

Electronic sport (esport) refers to competition in video games. Injuries in esports have hardly been studied so far. A total of 1229 e-athletes of all levels and genres answered a retrospective questionnaire about injuries and overuse damages that occurred in the course of their careers. The average age of the 1229 participants was 23.8 ± 5.5 years. A total of 198 (16.1%) of the e-athletes take part in competitions. The most common injury location was the trunk/spine (319, 26.0%) followed by the wrist region (225, 18.3%). Degenerative and overuse injuries were in the foreground. Professional athletes were injured more frequently than amateur athletes (p = 0.006). Tactical shooter players have significantly more injuries than sports game players (p = 0.021) and MMO (Massively Multiplayer Online) players (p = 0.042). E-athletes are just as susceptible to injury as athletes in traditional disciplines. The high injury rate is certainly not due to acute injuries but to overloading and overuse injuries, with a focus on the thoracocervical area and the upper extremities. Terms such as “Nintenditis”, “gamer’s thumb” and “PlayStation thumb”, which describe injuries caused by repetitive strain, are becoming increasingly common. Injuries in esports should be taken seriously, as they can cause long-term health problems in the event of overuse injuries. Prevention is a critical and promising approach for such a young patient clientele, especially in a sport that is growing so rapidly and is unknown to the majority. Full article

Gun violence in U.S. schools not only causes loss of life and physical injury but also leaves enduring psychological trauma, damages property, and results in significant economic losses. One way to reduce this loss is to detect the gun early, notify the police as soon as possible, and implement lockdown procedures immediately. In this project, a novel gun detector Internet of Things (IoT) system is developed that automatically detects the presence of a gun either from images or from gunshot sounds, and sends notifications with exact location information to the first responder’s smartphones using the Internet within a second. The device also sends wireless commands using Message Queuing Telemetry Transport (MQTT) protocol to close the smart door locks in classrooms and announce to act using public address (PA) system automatically. The proposed system will remove the burden of manually calling the police and implementing the lockdown procedure during such traumatic situations. Police will arrive sooner, and thus it will help to stop the shooter early, the injured people can be taken to the hospital quickly, and more lives can be saved. Two custom deep learning AI models are used: (a) to detect guns from image data having an accuracy of 94.6%, and (b) the gunshot sounds from audio data having an accuracy of 99%. No single gun detector device is available in the literature that can detect guns from both image and audio data, implement lockdown and make PA announcement automatically. A prototype of the proposed gunshot detector IoT system, and a smartphone app is developed, and tested with gun replicas and blank guns in real-time. Full article

Deep reinforcement learning (DRL) has been widely adopted to solve decision-making problems in complex environments, demonstrating high performance across various domains. However, DRL-based FPS agents are typically trained with a traditional, monolithic policy that integrates heterogeneous functionalities into a single network. This design hinders policy interpretability and severely limits structural flexibility, since even minor design changes in the action space often necessitate complete retraining of the entire network. These constraints are particularly problematic in game development, where behavioral characteristics are distinct and design updates are frequent. To address these issues, this study proposes a Modular Reinforcement Learning (MRL) framework. Unlike monolithic approaches, this framework decomposes complex agent behaviors into semantically distinct action modules, such as movement and attack, which are optimized in parallel with specialized reward structures. Each module learns a policy specialized for its own behavioral characteristics, and the final agent behavior is obtained by combining the outputs of these modules. This modular design enhances structural flexibility by allowing selective modification and retraining of specific functions, thereby reducing the inefficiency associated with retraining a monolithic policy. Experimental results on the 1-vs-1 training map show that the proposed modular agent achieves a maximum win rate of 83.4% against a traditional monolithic policy agent, demonstrating superior in-game performance. In addition, the retraining time required for modifying specific behaviors is reduced by up to 30%, confirming improved efficiency for development environments that require iterative behavioral updates. Full article

Acceptance and Commitment Therapy (ACT) has been widely applied in clinical contexts; however, its systematic adaptation to elite sports, particularly precision-based disciplines such as shooting, remains underexplored. The present study aimed to develop and preliminarily validate an ACT-based psychological training program—the Acceptance and Commitment Performance Training for Shooters (ACPT-S)—by reframing ACT from a therapeutic intervention into a performance-oriented training framework. Using a multiphase formative evaluation design, a needs assessment was first conducted with 28 elite and collegiate shooters to identify sport-specific psychological demands. Based on these findings, a ten-session ACPT-S program was developed by integrating the six core ACT processes with shooter-specific routines, embodied exercises, and performance-relevant metaphors. The program was subsequently examined through two pilot studies: Phase 1 with four collegiate/corporate athletes and Phase 2 with 15 national-level shooters. Data were collected via session reflections, focus group interviews, and expert panel evaluations, and the Content Validity Ratio (CVR) analysis was used to assess conceptual clarity and implementation feasibility. The results indicated that ACPT-S was perceived as both feasible and contextually appropriate, with athletes reporting improvements in attentional focus, emotional acceptance, value-based motivation, and reduced anxiety. Qualitative analyses demonstrated strong engagement with ACT principles and their functional integration into shooting performance contexts, while all program components achieved CVR scores of ≥0.80, indicating a strong expert consensus. Program refinements were guided by feedback related to activity sequencing, metaphor resonance and personalization strategies. Overall, this study reconceptualizes ACT as a performance-enhancement framework rather than a purely clinical approach and introduces the ACPT-S as a novel, theory-driven, and scalable psychological training model for precision sports, providing a robust foundation for future longitudinal and comparative research. Full article

Von Neumann’s minimax theorem defines optimal strategic unpredictability in zero-sum games. Empirical evidence from professional sports has been interpreted as positive behavioral evidence for minimax. In this article, we analyze the strategic optimality of offensive plays in the basketball endgame when a team has a final possession and trails by no more than a single basket. This final moment of the game most closely approximates the simultaneous-move conditions of a game where minimax theory applies. Using comprehensive NBA data from 2010 to 2025, we test for equality of success rates across shooter types (star vs. non-stars) and shot selection (two-point vs. three-point). Our analysis reveals systematic violations of minimax play that have intensified with basketball’s shift to three-pointers and higher expected points. In the final decisive moment of the game, we find that teams systematically overuse three-point shots even though the two-point attempt yields higher field goal percentages. In addition, teams over-rely on star players for the final shot; non-star two-point shots have been the top-performing endgame option in 2022–2025. Full article

This study provides a symmetrical, cross-genre comparison of human expertise transfer and “blind” artificial intelligence (AI) performance in audio-only gaming environments. Although previous research has focused on human performance in audio games and the feasibility of blind agents trained on auditory inputs separately, a direct comparison of these two forms of expertise is missing. We fill this gap with a robust experimental design, involving 37 human players (aged 18–44), grouped by gaming experience and specialized blind AI agents. We measured key performance variables, including win ratios, health differences, and task completion times across two genres: a fighting game (DareFightingICE) and a first-person shooter (SonicDoom). Our findings show a complex, task-dependent relationship. In DareFightingICE, expert humans (73.0% win ratio) significantly outperformed the AI (54.0% win ratio), demonstrating effective cognitive transfer. Meanwhile, the AI’s performance matched the overall human average (54.0% vs. 53.0%). Conversely, in SonicDoom, AI achieved superhuman speed in simple tasks (1.55 s vs. 5.35 s) but underperformed compared to expert humans in complex scenarios, highlighting that the AI’s proficiency is specialized but fragile, whereas human expertise is more robust and adaptable. The results provide practical insights for audio-rich game design and highlight the crucial need for AI models beyond reactive policies. Full article

First responders operate in high-stakes environments, demanding rapid, accurate decision-making. Recent research has provided recommendations on how Augmented Reality (AR) could be utilized to support their efforts. Building off of these studies, this paper presents EasyVizAR, an AR system designed to enhance situational awareness and operational efficiency in challenging indoor scenarios. Leveraging edge computing and advanced computer vision techniques, EasyVizAR addresses critical challenges, such as object detection, localization, and information sharing. This research details the system’s architecture, including its use of ParaDrop-based edge computing, and explores its application in rescue and active-shooter scenarios. We present our work in developing key features, including real-time object saliency cues, improved object detection, person identification, multi-user 3D map generation and visualization, and multimodal AR navigation cues. Full article

Blackground: Football performance analysis has grown rapidly in recent years, with increasing interest in advanced metrics to more accurately evaluate both individual and team performance. The aim of this study was to examine the utility of the Expected Shots Impact Timing (xSIT) metric as an indicator of shooting performance in English professional football, specifically in the men’s Premier League (PL) and the Women’s Super League (WSL). Methods: A total of 9831 shots from the PL (2015/16 season) and 3219 shots from the WSL (2020/21 season) were analyzed. Data were obtained from publicly accessible football databases. The variables examined included goals, Possession Value (PV), Expected Goals (xG), Expected Goals on Target (xGOT), and xSIT. All variables were normalized per match (90 min). Descriptive statistics, correlational analyses, and comparative analyses between leagues. Results: The WSL exhibited a significantly higher PV than the PL (p < 0.001), whereas the remaining metrics showed no significant differences between leagues (p > 0.05). Moreover, in the WSL, all performance indicators displayed very strong correlations with goals, while in the PL, similarly strong associations were observed, except for PV, which showed only a weak relationship. Conclusions: the xSIT metric, as an indicator of shooting performance, may be regarded as an influential factor in determining match outcomes across both leagues. Full article

Rewards play a key role in gamifying education, especially when learners perceive them as valuable. However, in many educational games, rewards often lack a meaningful impact or long-term appeal, which limits their ability to motivate user performance effectively. This study introduces a novel integrated reward system designed to increase the perceived value of educational rewards by allowing them to be used in a separate recreational game. The system was implemented using two Android-based applications: EduGym, a microlearning quiz-based educational game, and EduShooter, a top-down action shooter recreational game. Coins earned in EduGym quizzes can be used to upgrade characters and unlock content in EduShooter, forming a cross-game incentive. A user study involving 48 participants demonstrated that those with access to the integrated system responded more positively to EduGym’s reward mechanism and rated their overall game experience favorably. The reward system also enhanced learners’ perception of their educational achievements by linking them to meaningful in-game benefits. These findings suggest that integrating educational and entertainment games through a cross-game currency system can significantly strengthen the motivational appeal and perceived value of rewards in these games. Full article

Background: Action video games—particularly first-person-shooter (FPS), real-time-strategy (RTS), multiplayer-online-battle-arena (MOBA), and battle-royale (BR) titles—have been linked to enhanced visuospatial skills, yet their impact on brain structure remains unclear. Purpose: To examine, using a cross-sectional design, whether long-term exposure to high-speed genres is associated with variations in cortical thickness and white matter microstructure. Methods: Structural and diffusion MRI were acquired from 27 video-game players (VGPs) and 19 non-video-game players (NVGPs). FreeSurfer-derived cortical thickness and DSI-Studio quantitative anisotropy (QA) were compared between groups, co-varying for intracranial volume. All p-values were Holm–Bonferroni- and FDR-corrected; bootstrap 95% CIs are reported. Results: VGPs showed greater cortical thickness in right inferior and superior parietal, supramarginal, and precuneus cortices (η_p² = 0.12–0.21) and higher QA along right SOG–SPL and left SOG–IPL tracts. Conclusions: Frequent action gaming is associated with greater cortical thickness in the dorsal stream and enhanced occipito-parietal connectivity. However, causal inference is precluded; longitudinal work is warranted. Full article

Background: In recent years, complexity analysis has attracted considerable attention in the field of neural mechanism exploration due to its nonlinear characteristics, providing a new perspective for revealing the complex information processing mechanisms of the brain. In precision sports such as shooting, complexity analysis can quantify the complexity of activity in different areas of the brain and dynamic changes. Methods: This study extracted multiple complexity indicators based on microstate and traceability analysis and examined brain complexity during the shooting preparation stage and the brain’s reaction mechanisms under audiovisual limitations. Results: Microstate Lempel-Ziv complexity and microstate fluctuation complexity in low-light environment were significantly higher than those in normal environment. The complexity of the brain increases and then decreases during shooting. In low-light conditions, nine brain regions—insula R’, posterior cingulate R’, entorhinal, superior frontal L’, caudal anterior cingulate L’, rostral anterior cingulate L’, posterior cingulate R’, medial orbitofrontal L’ and rostral middle frontal R’—exhibited differential results. SSV-R_PHC-COG and SSV-R_LOF-SCORE showed strong negative correlations with behavioral indicators. Conclusions: First, during shooting, the processing of visual information mainly relies on the secondary cortex and visual connection functions, rather than the primary cortex. Furthermore, there are automated processes based on experience in shooting sports. Second, noise has little effect on shooting, but low light has a multifaceted impact on shooting. This is mainly reflected in difficulties in integrating sensorimotor information, excessive memory retrieval, reduced movement stability, triggering of negative emotions, and changes in shooting strategies. Full article