| Popis: |
Sport activity recognition has become one of the primary sport performance analysis contributions as it offers a notational data for tactical analysis and planning. In developing of activity recognition algorithm, searching for the best window segmentation size within time-series data is one of the parameters that contribute to the performance of algorithm in term of the accuracy rate. Yet, previous studies on activity recognition have implemented a different fixed size of window in segmenting the specific activities and the performance of activity recognition algorithm along with the changes of window size is still uncertain. Thus, this study is investigating the performance of field hockey activity recognition algorithm based on inertial sensor time-series data with different window segmentation size. The study was conducted on 11 subjects who worn the inertial sensors on their chest and waist while performing the six common field hockey activities which are passing, drive, drag flick, dribbling, receiving and tackles. The performance of each size of windows were observed and evaluated by using Cubic support vector machine (SVM). Among the different window sizes studied, this study found 1.5 s and 2.0 s are among the top in producing high accuracy rate for recognizing the field hockey activity that represent the movement from chest and waist with 89.6% and 91.4% accuracy respectively. |
