Abstract
Aerobics is the fusion of gymnastics, dance, and music; it is a body of a sports project, along with the development of the society. The growing demand for aerobics inevitably increases the demand for aerobics coach and teacher and has opened elective aerobics class which is an effective way of cultivating professional talents relevant to aerobics. Aerobics has extended fixed teaching mode and cannot conform to the development of the times. The motion prediction of aerobics athletes is a new set of teaching aid. In this paper, a motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of Things and the bidirectional long short term memory (BiLSTM) network. Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes. The inertial sensors were used for data collection and transmission of the data to the cloud platform through Ethernet. Then, the movement of aerobics athletes is recognized and predicted by the BiLSTM network. Based on the BiLSTM network and the attention mechanism, this paper proposes to solve the problem of low classification accuracy caused by the traditional method of directly summing and averaging the updated output vectors corresponding to each moment of the BiLSTM layer. The simulation experiment is also carried out in this paper. The experimental results show that the proposed model can recognize aerobics effectively.
Highlights
In the current era, aerobics [1, 2] is a popular sport around the world. e special aerobics course aims to train aerobics teachers or professionals with excellent aerobics skills and teaching ability
Aerobics Posture Estimation Based on Inertial Sensor. e ZigBee wireless sensor network is responsible for collecting the data of the inertial sensors and transmitting it to the cloud platform via Ethernet. e cloud analyzes the data and inputs it to the bidirectional long short term memory (BiLSTM) network [24] for aerobics recognition
A motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of ings and the bidirectional long short term memory network
Summary
Aerobics [1, 2] is a popular sport around the world. e special aerobics course aims to train aerobics teachers or professionals with excellent aerobics skills and teaching ability. At the moment, most colleges and universities have started special aerobics classes and continue the traditional teaching model. Aerobics has become a professional course in schools, and more and more students are fond of this sport. Aerobics teaching courses in various colleges and universities are set up as elective, compulsory, and special courses according to the requirements of the new curriculum reform. E famous Swedish modern gymnast Meekman defined aerobics as “the rhythmic body movement into the gymnastic movements, which can reflect the creativity of movements and conform to the progress of the times as a new form of gymnastics.”. E popularization of aerobics teaching in various colleges and universities makes more students understand and love the aerobics movement. Fan Yang, a scholar, thoroughly analyzed aerobics’ teaching situation in colleges and universities in Beijing from teaching content and teaching methods. e primary deficiencies in aerobics teaching are analyzed: the students’ lack of understanding and understanding of aerobics class causes the students to be not optimistic about aerobics learning and makes the teaching quality and efficiency of aerobics’ more efficient courses are not high
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
