AI has made its way in sports in numerous instances, including activities such as strategy estimation and building, game prognoses and analysis, as well as forecasts. Businesses like the betting company Mostbet can utilize AI to make predictions, enabling fans to track their favorites with greater precision.
Yet, one of the most critical applications of AI in sports is estimating player poses during games and training, and using these data for predicting the risk of trauma, building therapeutic routines tailored to the needs of individual players, and more.
In this article, we will cover this topic in more detail. Let’s get started!
What Is Human Pose Estimation Technology?
Human pose estimation technology is an AI-based computer vision technology designed to recognize and track specific points in a person’s posture, and then calculate and estimate their poses.
The model that estimates and tracks poses has to be previously trained on thousands of footage of sports events and prepared in collaboration with physical therapists to label poses as good or bad.
This way, the model has a library with an array of variations of different motions to learn from and to take as a basis.
Then a model tries to identify the key points on the human body. This includes limbs and joints, which are virtually attached to key points, and based on those points, the position gets estimated.
The technology allows for tracking poses in real-time and generating alerts when the pose deviates from a golden standard.
When sportsmen’s motions are analyzed in recordings, this can be used to build recommendations for gameplay, training, and rehabilitation.
How Is AI Trained for Human Pose Estimation?
Human pose estimation is a machine learning technology, meaning that the algorithm has to be trained on large datasets. Several premade datasets can be used for training, including HumanEvam, Coco, MPI Human Pose, and Human3.6M.
These databases provide the basis for the training, but they don’t include sport-specific data and other elements required for a more detailed approach. Therefore, the additional training on the proprietary data can be used to enhance the models and make them applicable for various use cases.
The Uses of Human Pose Estimation
As the model is trained and ready, it can be integrated within software as simple and usual as a mobile phone app and used by sportsmen, coaches, and physical therapists for various purposes. The use cases may include:
Performance analysis
Human pose estimation can be used for technique refinement. The technology analyzes the athletes’ motion during training or competition and estimates various elements, such as running gait, swimming strokes, or golf swings.
Then, the app takes kinematics measurements, such as joint angles and velocity, and establishes how these influence the player’s performance.
Then, they can help generate recommendations on how to improve poses to enhance performance.
Injury Prevention and Rehab
The technology can detect unsafe patterns in players’ behavior and habits, helping to correct them and lower the risk of trauma due to risky game techniques.
Additionally, the technology can help coaches identify the signs of muscle fatigue during training and develop personalized training plans.
In rehabilitation, the technology enables monitoring of the range of motion and movement patterns during recovery from injuries, providing better visibility of progress and potential improvements in the rehabilitation process.
Coaching and Training
In coaching and training, the human pose estimation technology can help coaches see how actual athletes’ forms differ from the ideal and how to improve them.
This way, the coaches can develop individual improvement programs for each player on the team to achieve the best possible performance.
The technology can also be used for skill acquisition. A new player can begin training by adopting best practices from the outset. In case there are difficulties with some motions, the player can correct them from the start and improve at once.
Development of Autonomous Coaching Systems
Human pose monitoring systems can contribute to the development of robotic coaching systems that provide recommendations, serve as coach assistants, and aid in game judging.
Also, the technology can have applications in the development of the AR/VR training sets. When developed, such technology can provide immersive experiences for players and amateurs to train, acquire habits, and receive real-time recommendations on their postures.
Such training sets can be utilized in various sports, including tennis, cricket, football, and others.
Summing up
The role of AI in sports is growing, evolving in various ways. Yet, human pose estimation technology is one of the applications that can have an enormous influence on sports. From professional sports to amateur training, utilizing the expertise of high-level coaches can help players improve significantly, accessible with just a few taps.
