This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase using pose estimation and accurately measures the shooting time by detecting the bowstring within the athlete’s facial bounding box, utilizing Canny edge detection and the probabilistic Hough transform. To ensure stability, low-pass filtering was applied to both the facial bounding box and pose estimation results, and an algorithm was implemented to handle intermittent bowstring detection due to various external factors. The proposed method was validated by comparing its results with expert manual measurements obtained using Dartfish software v2022 achieving a mean absolute error (MAE) of 0.34 s and an $R^2$ score of 0.95. This demonstrates a significant improvement compared to the bowstring-only method, which resulted in an MAE of 1.4 s and an $R^2$ score of 0.89. Previous research has demonstrated a correlation between shooting time and arrow accuracy. Therefore, this method can provide real-time feedback to athletes, overcoming the limitations of traditional manual measurement techniques. It enables immediate technical adjustments during training, which can contribute to overall performance improvement.

The purpose of this study was to quantify the bullet impact locations of the men’s 10 m air pistol competition and propose objective metrics for evaluating shooting techniques. We automatically collected data from the top 20 competitors’ shooting results using computer vision techniques. Metrics such as x-variance, y-variance, covariance, x-mean, y-mean, root mean square error (RMSE), x-mean score, and y-mean score were computed to investigate correlations among rankings, left–right and up–down shot groups, aiming relationships, and precision. Covariance analysis revealed significant interactions between horizontal and vertical aiming, highlighting the importance of balanced coordination between these directions for high performance. Athletes with lower covariance values, indicating less variation between horizontal and vertical aiming, tended to achieve higher rankings. Additionally, top-ranked athletes exhibited lower RMSE values, underscoring the importance of precision in achieving high scores. In conclusion, this study analyzed the correlation between x and y through covariance, examined its relationship with competition rankings, and proposed new indicators for training and performance enhancement. This study is novel in that it provides quantitative data to correct poor aiming and shooting habits by performing a covariance-based bidirectional correlation analysis, rather than simply analyzing bullet impact locations in a single horizontal or vertical direction. Our approach establishes a foundation for more data-driven and objective evaluations in the sport of shooting.

In speed skating, the number of strokes in the first 100 m section serves as an important metric of final performance. However, the conventional method, relying on human vision, has limitations in terms of real-time counting and accuracy. This study presents a solution for counting strokes in the first 100 m of a speed skating race, aiming to overcome the limitations of human vision. The method uses image recognition technology, specifically MediaPipe, to track key body joint coordinates during the skater’s motion. These coordinates are calculated into important body angles, including those from the shoulder to the knee and from the pelvis to the ankle. To quantify the skater’s motion, the study introduces generalized labeling logic (GLL), a key index derived from angle data. The GLL signal is refined using Gaussian filtering to remove noise, and the number of inflection points in the filtered GLL signal is used to determine the number of strokes. The method was designed with a focus on frontal videos and achieved an excellent accuracy of 99.91% when measuring stroke counts relative to actual counts. This technology has great potential for enhancing training and evaluation in speed skating.

Accurate scoring in archery is essential for objective performance evaluation and training feedback. However, existing systems are often limited by high cost, complex installation, or reliance on manual inspection. This study introduces a low-cost, image-based automatic scoring system optimized for outdoor environments, integrating conventional computer vision techniques with a deep learning–based heatmap regression framework. The system detects the target, applies circular geometric normalization, and employs a convolutional hourglass network to predict arrow impact points as heatmaps. A peak detection process then estimates scores based on the Euclidean distance from the target center. Localization error is measured in centimeters using a calibrated pixel-to-centimeter scale derived from a 10-point scoring zone diameter. Experimental results showed a mean localization error of 1.77 mm, with 67.83% of predictions within 2 mm, demonstrating reliable sub-half-centimeter accuracy under varying lighting conditions and partial occlusions. The system also provides directional feedback, supporting its application in both performance-monitoring and targeted-training enhancement. Overall, the approach offers a scalable, field-ready solution for archery that can be easily extended to other target-based sports.

This study investigates the effects of heart rate and arm tremor on performance in elite archery, using non-contact physiological monitoring from real Olympic competition footage. A total of 50 video segments were extracted from publicly available international broadcasts, comprising athletes of various backgrounds. From these, heart rate signals were estimated via remote photoplethysmography (rPPG) from facial regions, and micro-movements were quantified from right and left arm regions using feature point tracking. Ordinal logistic regression was employed to evaluate the relationship between biometric variables and archery scores (10, 9, ≤8 points). Results showed that elevated heart rate ($\beta$ = −0.1166; $p<$ 0.001) and greater right-arm movement ($\beta$ = −6.1747; p = 0.008) were significantly associated with lower scores. Athletes scoring 10 points exhibited significantly lower heart rate ($p<$ 0.001) and reduced right-arm tremor (p = 0.010) compared to others. These findings support the hypothesis that physiological arousal and biomechanical instability impair performance, and they further demonstrate the feasibility of contactless monitoring in real competition environments. The proposed method enables objective, in-game performance evaluation and supports the development of personalized training systems for precision sports.