Aim: This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature search was conducted across five databases (PubMed/MEDLINE, Web of Science, IEEE Xplore, Scopus, and SPORTDiscus), encompassing studies published between January 2015 and December 2024. After screening 3248 articles, 73 studies met the inclusion criteria (Cohen’s kappa = 0.84). Data were collected on AI techniques, biomechanical parameters, performance metrics, and implementation details. Results revealed a shift from traditional statistical models to advanced machine learning methods. Based on moderate-quality evidence from 12 studies, convolutional neural networks reached 94% agreement with international experts in technique assessment. Computer vision demonstrated accuracy within 15 mm compared to marker-based systems (6 studies, moderate quality). AI-driven training plans showed 25% accuracy improvements (4 studies, limited evidence). Random forest models predicted hamstring injuries with 85% accuracy (3 studies, moderate quality). Learning management systems enhanced knowledge transfer, raising coaches’ understanding by 45% and athlete adherence by 3.4 times. Implementing integrated AI systems resulted in a 23% reduction in reinjury rates. However, significant challenges remain, including standardizing data, improving model interpretability, validating models in real-world settings, and integrating them into coaching routines. In summary, incorporating AI into sports biomechanics marks a groundbreaking advancement, providing analytical capabilities that surpass traditional techniques. Future research should focus on creating explainable AI, applying rigorous validation methods, handling data ethically, and ensuring equitable access to promote the widespread and responsible use of AI across all levels of competitive sports.

Although studies have indicated that the prior use of video feedback and music listening separately improves physical performance and positive feelings in various sports, to our knowledge, no studies have investigated their combined effect in combat-sports-specific tasks. The aim of this study was to determine the combined effect of listening to preferred music and video feedback on aerobic and anaerobic performance in male kickboxers. In a counterbalanced crossover study design, twenty kickboxers underwent three kicking exercises under one of three conditions: (1) control condition, (2) combined listening to preferred music and video feedback, and (3) video feedback during 10-min of rope warm-up. Kickboxers performed a ten-second kicking test, multiple ten-second kick test, and progressive taekwondo test. The total number of kicks, fatigue index, heart rate, rate of perceived exertion, and feeling scale were measured. The combined music and video feedback condition improved the number of kicks with a better positive feeling scale (F ≥ 7.4, p ≤ 0.002, η_p² ≥ 0.28) than the video feedback and control conditions in all three kicking exercises, while the video feedback alone led to better kick performances and a better feeling scale than the control condition in the ten-second and multiple ten-second kicking tests (p ≤ 0.016). The combined listening to preferred music and video feedback condition was more effective at enhancing the positive feeling scale and repeated roundhouse kick performance. Future investigations should examine the application of video feedback and listening to music in various kickboxing tasks including punches and kicks.

Both music and endpoint knowledge of exercise have been shown to independently influence exercise performance. However, whether these factors work as synergists or counteract one another during exercise is unknown. The purpose of this study was to determine the single and combined effect of listening to preferred music and types of endpoint knowledge on repeated countermovement jump (CMJ) test performance. Twenty-four (n = 24) current or previously competitive basketball players underwent CMJ testing under the following endpoint knowledge conditions: (1) unknown/no knowledge, (2) knowledge of the number of jumps, and (3) knowledge of exercise duration. For each of these, participants listened to either their preferred music or no music during the duration of testing. For the exercise portion, participants completed repeated CMJs where participants were encouraged to jump as high as possible with jump height, contact time, and flight time as outcomes. Rate of perceived exertion (RPE) and feeling scale were measured before and after exercise. The results showed that, regardless of knowledge type, preferred music resulted in a significant decrease in both contact time and flight time (F ≥ 10.4, p ≤ 0.004, and η_p² ≥ 0.35), and a significant improvement of jump height (F = 11.36, p = 0.001, and η_p² = 0.09) and feeling scale ratings (F = 36.9, p < 0.001, and η_p² = 0.66) compared to no-music condition, while RPE was not significantly affected. Regardless of the presence of music, knowledge of the number of jumps and duration resulted in lower contact time (p < 0.001, 0.9 < d < 1.56) versus unknown condition during CMJs. Moreover, a significant decrease in RPE values was found during prior endpoint knowledge of number (p = 0.005; d = 0.72) and duration (p = 0.045; d = 0.63) compared to unknown condition. However, feeling scale ratings were not significantly affected. Moreover, no interactions with significance findings were found for any parameters. Overall, data suggest that listening to music and endpoint knowledge alter exercise responses in basketball players, but they do not interact with one another.

This investigation aimed to examine the effect of verbal encouragement teacher to student (VETS) versus verbal encouragement student to student (VESS) on physical performance and affective responses during different modalities of sprint tests in active male students. In a randomized crossover design, twenty-two male sport science students (age: 21 ± 1.2 years, body height: 1.77 ± 0.3 m, body mass: 76.6 ± 2.1 kg, BMI: 22.9 ± 1.3 kg·m⁻²) performed linear and change-of-direction sprint tests under one of three conditions: (1) VETS; (2) VETS; (3) no verbal encouragement. In each condition, participants performed the 20 m sprint test, the 10 × 2 sprint test, and the L sprint test. The assessed parameters comprised physical performance, rating of perceived exertion (RPE), and a feeling scale. Post hoc test analysis indicates a significant increase in physical performance during VETS and VESS conditions compared to the control condition due to a decrease in sprint in line 20 m (VETS: p < 0.001, d = 0.55; VESS: p = 0.016, d = 0.41), sprint 10*2 (VETS: p < 0.001, d = 0.64; VESS: p = 0.05, d = 0.36), and sprint L (VETS: p = 0.001, d = 1.19) times compared to the control condition. Moreover, the feeling score was greater after VETS compared to other conditions (p = [<0.001–0.001], d = [0.77–1.18]). In addition, the RPE had no effect on sprint performance between the different conditions. It is indicated that VETS, rather than VESS, is a more significant and effective way to increase effort intensity and positive feelings during sprinting modalities.