华中师范大学学报（自然科学版）2026，Vol.60Issue(2)：321-329,9.DOI:10.19603/j.cnki.1000-1190.2026.02.014
基于K-means算法对高水平篮球运动员投篮过程中神经—肌肉控制策略的特征研究
Characteristics of neuromuscular control strategies during shooting in elite basketball players based on the K-means clustering algorithm
摘要
Abstract
In this study neuromuscular control strategies during basketball shooting across skill levels were examined.Kinematic,kinetic,and surface electromyography(sEMG)data were collected synchronously from 14 high-level and 14 novice players using a three-dimensional motion capture system,force platforms,and wireless sEMG.Using Kendall's myotome distribution theory,an inverse-mapping model was established to estimate spinal α-motoneuron pool activity from sEMG.Muscle synergies were then extracted using non-negative matrix factorization(NMF)followed by K-means clustering.Results indicated that,during set shots,the high-level group showed greater activation at C5-T3 and T9-T10 during the rhythmical phase and at S3 during the rhythmical phase than the novice group(p＜0.05).In contrast,during jump shots,C6 output during the rhythmical phase was lower in the high-level group(p＜0.05).The third synergy module differed in spatial structure between groups(p＜0.05):in the high-level group it predominantly involved lower-limb muscles,whereas in the novice group it mainly involved upper-limb muscles.The high-level group also exhibited an additional synergy module that primarily involved lower-limb muscles.Muscle contributions differed between groups in the first and third synergy modules(p＜0.05).During jump shots,the proportion of combined synergies was lower in the high-level group than in the novice group(p＜0.05).Collectively,these results suggested that high-level players rely more on lower-limb activation during force production and recruit an additional synergy related to lower-limb extension,whereas novice players rely more on upper-limb activation and exhibit a higher proportion of combined synergies.Accordingly,training for novice players should emphasize coordination between lower-limb extension and upper-limb force generation to improve force transfer efficiency and shooting performance.关键词
篮球投篮/肌肉协同/运动控制/机器学习Key words
basketball shooting/muscle synergy/motor control/machine learning分类
社会科学引用本文复制引用
周启钊,苏荣海,潘正晔,马运超..基于K-means算法对高水平篮球运动员投篮过程中神经—肌肉控制策略的特征研究[J].华中师范大学学报（自然科学版）,2026,60(2):321-329,9.基金项目
教育部人文社会科学基金青年项目(19YJC890030) （19YJC890030）
北京市社会科学基金项目(22YTB009). （22YTB009）
