Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance video quality. The model then uses a bifurcated neural network to extract detailed features, leading to a densely connected neural network with a specific activation function for categorizing videos. We tested our model on a High-Definition Sports Video Dataset covering over 20 sports and a low-resolution dataset. Our model outperformed established classifiers like DenseNet, VggNet, Inception v3, and ResNet-50. It achieved high precision (0.9718), accuracy (0.9804), F-score (0.9761), and recall (0.9723) on the high-resolution dataset, and significantly better precision (0.8725) on the low-resolution dataset. Correspondingly, the highest values on the matrix of four traditional models are: precision (0.9690), accuracy (0.9781), F-score (0.9670), recall (0.9681) on the high-resolution dataset, and precision (0.8627) on the low-resolution dataset. This demonstrates our model’s superior performance in sports video classification under various conditions, including rapid motion and low resolution. It marks a significant step forward in sports data analytics and content categorization.

Diabetic peripheral neuropathy (DPN) increases fall risk in diabetics. Due to varying variables used to assess fall risk, the impact of exercise on fall prevention remains inconsistent. This study reviews and compares the effects of different exercises on fall risk among older adults with DPN. A comprehensive literature search was conducted in PubMed, EBSCO, Web of Science, and Cochrane Library up to February 17th, 2025. Inclusion criteria were: older adults with DPN; exercise intervention only, an inactive or non-exercising control group, and randomized controlled trials with outcome variables: timed up and go (TUG) time, gait speed, Berg Balance Scale (BBS) score, one-legged standing (OLS) time with eye open (EO) and closed (EC). The mean difference (MD) and 95 % confidence interval (CI) were calculated. A total of 21 articles included five exercise types: balance exercise (BE), multi-component exercise (ME), strength exercise (SE), whole-body vibration (WBV) and foot-ankle functional training (FT). BE reduced TUG time (MD = −1.47, 95 % CI = −1.79 to −1.15) and increased gait speed (0.11, 0.04–0.18), BBS score (0.93, 0.49–1.37), and OLS time (EO: 2.72, 1.86–3.58; EC:1.58, 1.0–2.17). ME reduced TUG time (−1.71, −2.26 to −1.17) and increased BBS score (2.0, 1.28–2.72) and OLS time (EO: 7.07, 4.35–9.79; EC: 2.61, 1.28–3.94); SE reduced TUG time (−1.45, −2.75 to −0.15) and increased gait speed (0.09, 0.06–0.12); WBV increased OLS time (EO: 1.94, 1.32–2.56; EC: 1.86, 0.16–3.56) but did not affect TUG time or gait speed. FT did not affect TUG time or gait speed. Exercise reduced fall risks among older adults with DPN. BE and ME were effective in reducing fall risks, followed by SE. WBV improved static balance but failed in dynamic balance. FT showed limited effects on fall prevention and was not recommended.

Background: Advancements in sports materials have led to the creation of innovative fabrics aimed at enhancing athletic performance and reducing the risk of sports-related injuries. Graphene-based composite fibers, with superior far-infrared (FIR) emissivity, are emerging as a promising material in sportswear. This study explores the effects of graphene-based FIR compression garments on aerobic exercise capacity. Results: A total of 15 healthy, recreationally active male university students (aged 18–25 years) participated in this double-blind, randomized crossover trial. Each participant completed two incremental treadmill tests while wearing either graphene-based FIR compression garments or control garments, with a 7-day washout period between sessions. Results showed significantly longer exercise durations (38.4 s, p < 0.001) and extended time to anaerobic threshold (37.7 s, p < 0.001) in those wearing graphene-based FIR garments compared to those wearing control garments. The maximum heart rate was significantly lower in the graphene group (198.8 ± 7.8 bpm vs. 200.3 ± 7.5 bpm, p < 0.05), with reduced heart rates at the same exercise intensity (176.9 ± 8 bpm vs. 179.8 ± 7.8 bpm, p < 0.05). No significant differences in Maximal Oxygen Uptake (VO2max) were observed between the two groups. Conclusions: Graphene-based FIR compression garments significantly enhance aerobic performance by improving endurance, likely due to improved peripheral blood circulation and reduced cardiac load. These findings highlight the potential of graphene-based fibers as a disruptive innovation in sportswear. Further research with larger sample sizes is warranted to fully explore their benefits.