Research on Image-Based Movement Accuracy Monitoring of Aerobics

Abstract

The movement accuracy monitoring of aerobics is mostly performed through three-dimensional reconstruction of aerobic movements. The feature extraction of aerobics is based on the optimal classification decision function, which extracts all the features of aerobics and thus reduces the accuracy of aerobics monitoring. In order to extract the aerobic motion in the background with higher accuracy, a new image-based monitoring method is proposed. First, the Kinect depth image acquisition method is used to preprocess the image, and then Hog3D is used to extract aerobic movement features and analyze the extraction results. This new method solves the problem of video content classification in aerobics precision monitoring. The Adaboost method in probability statistics is used to identify the accuracy of aerobic motions. This paper uses probability function to link the postures of aerobics and forms an action sequence and its ergodic function to take the maximum value of an aerobic exercise. The accuracy of aerobics is monitored by using the method of level by level proportional example. The experimental results show that this method can effectively improve the accuracy of aerobic track monitoring, reduce the energy consumption of aerobic movement accuracy monitoring, and has good use value.

IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes

“…erefore, to stimulate the degree of participation of students in learning is the main task. erefore, it is very important to accurately identify and evaluate aerobics movements [19][20][21][22][23] to assist teaching so that students can learn movement routines faster, stimulate their learning potential, and promote improved aerobics courses' quality.…”