| dc.description.abstract | With the rise of artificial intelligence and the improvement of computer hardware performance in recent years, the human action recognition (HAR) has gradually been popular, especially in Computer Vision and Pattern Recognition. The application has been widely developed in various field. For example, games, tracking and surveillance systems, smart environment, medical field etc.
This thesis is aimed at fitness behavior recognition, taking deadlift as an example. This exercise is a multi-joint movement, however beginners usually cause injury due to their incorrect concept and wrong posture. One of the purpose is for giving users some professional advice about fitness. The other is the system for the gym can share the work of the fitness instructor, so coaches can focus on the more professional teaching content to their students.
We propose a multi-modality deadlift recognition and application system, including Kinect camera and inertial sensors (x-OSC). Using Time-Variant Skeleton Vector Projection method and feature concatenated method before we feed features to our network. Then, we use the Long short-term memory (LSTM) network, a type of recurrent neural network, as classifier. The dataset we used is VAP multi-modalities fitness behavior dataset. This dataset is we proposed, and contain 6 fitness behavior. Using multi-modality data can achieve a good recognition accuracy effectively and the applications of our system can also analyze uses results effectively.
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