Accessible thermal imaging/AI method optimises sports performance | Imaging and Machine Vision Europe

Oct 14, 2024

Researchers combined video from thermal cameras with AI-based digital processing to enhance weightlifting training. Image: Laura Viafora, Universidad de Concepción

A new method that combines video from thermal cameras with AI-based digital processing could optimise performance and safety in a variety of sport and health applications.

While most current methods use before-and-after snapshots, the new method can track muscle activation and detect areas of strain or fatigue.

The new method can be used with an inexpensive thermal camera attached to a smartphone or a high-end thermal device, improving accessibility for different training needs.

“We developed an approach that analyses a complete thermal video recording while automatically obtaining the relevant parameters,” said research team leader Laura Viafora from Universidad de Concepción in Chile. “By analysing the entire movement continuously, rather than just specific moments, we can track the progression of temperature variation in real-time and understand what happens throughout the entire exercise.”

In order to track small changes in temperature and body positioning throughout an exercise, the researchers developed data processing algorithms and then used Google MediaPipe artificial intelligence software to identify individuals and their body parts within the images and extract the required information.

To obtain temperature data, the researchers identified the body part of interest and then converted the pixel colour information into temperature values. For positional information, key points in the joints were identified and used to calculate the corresponding angles.

Finally, to study the barbell’s movement, they detected the weight plates in the scene and then recorded the locations of their central points in each frame. Repeating this process for all frames allowed the researchers to produce graphs and frame-by-frame images tracking positional and temperature changes throughout the exercise.

To test the new method, they recorded athletes using a low-end thermal camera attached to a smartphone, a high-end stand-alone thermal camera and a conventional camera. This approach allowed them to compare results using various cameras and evaluate the effectiveness of body detection and angle estimation. The videos acquired with the various cameras were all uploaded to a computer for analysis.

These initial tests showed positive results, demonstrating that the new method could be used to generate a colour-labelled sequence of thermal images from multiple camera types as well as reports on body part positions, which could be helpful for guiding athletes toward safer and more efficient practices.

Now that they have demonstrated the feasibility of this approach, the researchers want to use the method to analyse images of athletes from various disciplines, including conventional and paralympic sports.

They are also working to refine their algorithm so that it can provide actionable information to users. For example, if an athlete raises their arm, the system could provide real-time feedback on whether the movement was performed correctly.

“Our goal is to continue developing this concept so that it can be used by athletes and coaches,” said Viafora. “We also aim to refine this technology for applications in the healthcare field by providing specialists with thermal data and information about body position, which could contribute to more effective rehabilitation after an illness or injury.”