Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Abstract

With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.

Athletes' Action Recognition and Posture Estimation Algorithm Based on Image Processing Technology

“…By integrating action recognition and posture estimation techniques, applications can gain a deeper understanding of human behavior and movement patterns in various contexts, such as sports analysis, surveillance, human-computer interaction, and healthcare monitoring [3]. For instance, in sports analysis, these techniques can be used to track and analyze athletes' movements to provide insights into their performance and technique [4]. In healthcare, posture estimation can assist in assessing and monitoring patients' physical rehabilitation progress or detecting abnormalities in movement patterns indicative of certain medical conditions [5].…”

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

“…Ji et al (2022) proposed the encoder-decoder network (EDN) model based on a given image [9]. This network was used to predict various parameters of the parameterized model, thereby predicting the posture of the human body [10]. This model can simultaneously predict the posture and shape of the human body.…”

Design of Real-time Movement Guidance System Based on Blazepose on Mobile Terminal

“…Traditional rehabilitation training requires patients to be carried out in specific training sites under the guidance of professional therapists, and the effect is not easy to evaluate, which requires a lot of energy of doctors, and may not be able to meet the needs of patients. With the in-depth study of computer vision and human posture estimation algorithm, human posture estimation is used in various aspects of life, so we think that can have an app to detect human movement posture, and compared with the standard movement after the movement, so as to improve the user accuracy of purpose movement [2].…”