PURPOSE This study optimized stopword removal to enhance topic modeling performance. We propose an objective method combining normalized pointwise mutual information (NPMI) with median-based term frequency–inverse document frequency (TF–IDF) to automatically generate stopwords. METHODS Using text data from 443 research papers on “Taekwondo sparring,” we selected stopword candidates based on NPMI and identified 30 words with the lowest TF–IDF scores. We examined the impact of removing 1–30 stopwords on u_mass coherence scores. RESULTS The NPMI–TF–IDF method significantly improved coherence (R² = .456; p < .001). However, excessive removal led to diminishing returns, with the optimal coherence score (−11.442) achieved at 200 stopwords. In contrast, manually selected stopwords yielded a lower coherence score (−16.001). The findings indicate that integrating TF–IDF with NPMI effectively preserves meaningful words and outperforms PMI2 and PMI3 approaches. CONCLUSIONS Manual stopword selection can reduce reproducibility. Optimizing stopword removal based on domain-specific characteristics is essential. Future research should validate this method across diverse fields to establish a more generalizable standard.
PURPOSE This study aims to verify the reliability of a newly developed augmented reality (AR)-based agility measurement method by comparing its outcomes with those of conventional agility tests. METHODS The participants were 12 middle and high school weightlifting athletes. To assess the reliability between the initial field test and the subsequent AR-based test, a dependent t-test and correlation analysis were conducted for three variables: side-step (count), color catch (count), and color catch (average reaction time). RESULTS First, significant correlations were observed between the field and AR-based measurements for side-step (count), color catch (count), and color catch (reaction time), confirming the reliability of the AR-based method. Second, no significant difference was found between side-step (count) and AR side-step (count) (t = 1.910), indicating consistency between the two methods. Third, a significant difference was found between color catch (count) and AR color catch (count) (t = 4.750), suggesting lower consistency in this variable. Fourth, no significant difference was observed between color catch (average reaction time) and AR color catch (average reaction time) (t = 2.100), and the significant correlation coefficient (.579) further supports their consistency. CONCLUSIONS These results suggest that the newly developed AR-based agility measurement method yields reliable data and has practical applicability for sports performance assessment and training in field settings.
PURPOSE This study assessed elite Taekwondo athletes’ physical fitness and developed reference standards for both their basic and specialized physical fitness. METHODS Data for analysis were collected from 870 athletes: from national teams, 123 elite Taekwondo athletes from the Performance Analysis and Assessment System (PAAS) administrator website (1999–2020); from regional sports centers, 731 collegiate and general division elite Taekwondo athletes (2015–2019); and from Y University, 16 elite Taekwondo athletes. Through measurement items’ selection and categorization, 20 physical fitness items were selected for the reference standards’ development, including 9 for basic fitness and 11 for specialized fitness. Taekwondo weight classes were divided into two: light + middle (fin, fly, bantam, feather) and middle + heavy (light, welter, middle, heavy). RESULTS Descriptive statistics for basic and specialized physical fitness items were categorized by gender and athletes’ fitness level. The reference standards’ development was aligned with existing standards, integrating the Cajori physical fitness 5-levels. It also introduced minimum physical fitness reference standards and target achievement reference standards for evaluating elite Taekwondo athletes’ physical fitness. CONCLUSIONS The reference standards proposed here can serve as objective indicators in selection of national representative athletes and also provide foundational data to establish fitness goals and evaluate future elite athletes’ physical fitness.
