Recognition method of the shuttlecock kicking actions using wearable inertial sensors

Purpose This paper aims to propose a novel action recognition method for shuttlecock-kicking using wearable inertial sensors, focusing on improving recognition accuracy through the analysis of angular time-series features and the application of deep learning models. Design/methodology/approach Skeletal data was collected using wearable inertial sensors, and time-series data of key skeletal points relevant to shuttlecock-kicking actions was extracted. An angular time-series feature analysis method was proposed to describe motion characteristics by analyzing changes in angles between key skeletal points. These features were used as input for classification models, including convolutional neural network (CNN), long short-term memory (LSTM) and support vector machine (SVM), whose performance was evaluated based on accuracy, precision, recall and F1 score. Findings The proposed CNN model, using the angular time-series recognition method (ATRM), achieved an average accuracy of 0.9681 and an F1 score of 96.99%, surpassing other input methods including accelerometer and gyroscope data. The CNN model clearly demonstrated the superior potential of combining angular time-series features for more accurate and stable recognition of shuttlecock-kicking actions better than the LSTM and SVM models. Practical implications The method provided will benefit the real-time sports virtual games and wearable technology applications. Originality/value This work proposed a novel ATRM for action recognition using wearable sensors. The method enhances recognition accuracy and efficiency, providing strong ability for real-time sports analysis and wearable technology applications.