Sports Injury Rehabilitation Intervention Algorithm Based on Visual Analysis Technology

Abstract

Sports injuries of high-level athletes restrict the improvement of sports performance. Under this premise, an efficient and accurate sports injury assessment method is needed to detect potential sports injuries and conduct injury prevention training. Therefore, this paper proposes a novel sports injury prediction algorithm based on visual analysis technology. The proposed algorithm first takes the time-frequency of sensed data as the convolutional neural network (CNN) input. The one-dimensional time series collected by the sensor is converted into two-dimensional images using the Gram angle domain algorithm. The one-dimensional sensed data provides a new perspective and provides a basis for better use of convolutional neural networks and computer vision technology. Second, combining the residual network’s structure and advantages and hole convolution and multihole convolution kernel residual module is proposed. It improves the model’s ability to extract features at different scales while effectively controlling the parameter scale. Based on these modules, a single-sensor-based athlete action recognition algorithm is proposed. Several comparative experiments have been conducted on a public data set containing only acceleration sensors to verify the proposed algorithm’s effectiveness.

An Overview of Machine Learning Applications in Sports Injury Prediction

“…Using data collected from different body parts of athletes, KNN may analyze behaviors for athletes in unique sporting events. With this recognition model, patterns predisposing to injury can be determined, allowing for potential injury prevention [ 11 ]. In addition to their general use as comparison algorithms, a 2018 paper applied KNN as part of a larger model, including both K-means and support vector machine (SVM), for injury prediction [ 12 ].…”

“…Similarly, Chen et al describe a process of converting time series data acquired from player-worn sensors to two-dimensional images for analysis using a CNN. Notably, they validate using only acceleration data from a single sensor and were able to achieve acceptable levels of accuracy in classification [ 11 ]. Song et al in their 2020 study developed an optimized-CNN to predict and assess injuries in volleyball players.…”

Injury Prediction in Sports using Artificial Intelligence Applications: A Brief Review

“…KNN analyzes the data it gathers from various body parts of athletes to get to particular conclusions about the behaviors such athletes exhibit during certain sports events. Patterns that put a person at risk for harm may be identified using this recognition model, which opens the door to the possibility of injury avoidance [19]. In addition to its broad use as contrast procedures, KNN was utilized in the context of injury prediction in a study published in 2018 [20] that used a bigger model that included K-means and SVM.…”

An Improved Neural Network Model for Early Detection of Joint Injuries in Tai Chi Sports

“…Hinton first proposed deep learning in 2006, and it has been applied to the field of joint injury diagnosis because of its powerful automatic feature extraction capabilities [15]. CNN in deep learning is a supervised deep learning technique that can accomplish end-to-end joint injury detection without preprocessing the original obtained fault data [16][17][18][19][20][21]. It is one of several deep learning algorithms that may be used to diagnose joint injuries.…”

“…According to research and surveys, the total number of fitness qigong stations in the nation reached 27,838, as by the end of 2015, with more than 1.2 million individuals visiting the stations and more than 3.52 million practitioners. When looking at Taijiquan and Fitness Qigong in their present forms, they meet the needs of people all over the globe, and they may be able to meet the health needs of people all over the world, as well as the requirements of the Chinese cultural inheritance if they are practiced properly [20,21].…”