物理医学与康复
惯性参考系
惯性测量装置
心理学
航空学
计算机科学
人工智能
医学
工程类
物理
量子力学
作者
Yuki Masui,Nobuyoshi Hirotsu,Yu Shimasaki,Masafumi Yoshimura
标识
DOI:10.1177/17543371241278032
摘要
This study aimed to classify the movements of female soccer players during matches using raw data measured by inertial measurement units (IMUs). Twelve collegiate female soccer players were equipped with IMUs (100 Hz), and raw triaxial acceleration data from eight official matches were analyzed. The measurement data were separated every 3 s, and a Fast Fourier Transform (FFT) was performed. After FFT, the Euclidean distances between the data when the players were in the stationary state and other states were calculated to classify the movements of the players using the k-means method. The data of the clustering numbers classified as the stationary state were eliminated after analyzing the movements of players by video filming the matches. After classification, the average Euclidean distances between the stationary state and other movements were calculated. Consequently, the results showed that the upward and downward directions of the raw data affected the classification. Using the methods of this study, it was also shown that the distribution of Euclidean distances differed from player to player. Our findings indicate that the method used in this study can be used to classify and characterize the movements of female collegiate soccer players.
科研通智能强力驱动
Strongly Powered by AbleSci AI