Swing movements and muscle strength are essential for mastering badminton techniques. Traditionally, students learn badminton through their instructor's physical demonstration, verbal instructions, and small group activities. To enhance students' learning experience and assist badminton instructors more effectively, this study proposes an alternative teaching approach that utilizes a Badminton Teaching-Assisted System (BTAS) to teach swing movements. The system uses a gesture control armband to collect electromyography and gyroscope signals from professional players, which are then used to build a machine learning model based on the back propagation neural network. The model can be used as a scoring tool to assess the correctness of the student's swing movements and muscle strength. A total of 87 students participated in an eleven-week experiment focusing on two fundamental badminton skills: The backhand push and backhand lift. The results demonstrated a significant improvement in the correctness of the muscle strength during swing movements for the experimental group, compared to the control group. The BTAS can help to enhance the effectiveness of badminton instruction, thereby improving students' swing techniques. Future research could expand upon this study to develop more precise artificial intelligence-based sports assistance systems, which would facilitate personalized teaching and learning in physical education.