Challenges of Human Pose Estimation in AI-Powered Fitness Apps

Challenges of Human Pose Estimation in AI-Powered Fitness Apps

In this article, author discusses the human pose estimation solution powered by AI technologies and the challenges faced in online fitness apps which use the pose estimation to predict the position of the human body based on an image or a video containing a person.

Fitness is a trend today. Every year the revenue of the fitness industry grows by 8.7%, according to the Wellness Creatives report, and fitness apps have not spared this field.

There are many cases of how technologies might help to improve your body - from tracking exercise activity to adjusting nutrition. The question is how much better can such apps help improve the performance of physical exercises compared to human coaches?

Artificial Intelligence (Al, a broad name for a group of advanced methods, tools, and algorithms for automatic execution of various tasks) has invaded practically all functional areas of business over the years. Pose estimation is among the most popular solutions that AI has to offer; it is used to determine the position and orientation of the human body given an image containing a person. Unsurprisingly, such a useful tool has found many use cases, for instance it can be used in avatar animation for Artificial Reality, markerless Motion Captureworker pose analysis, and many more.

With the arrival of human pose estimation technology, the fitness technology market has been filling up with AI-based personal trainer apps. Some examples of applying pose estimation in fitness are KaiaVAI Fitness CoachAlly apps, or the Millie Fit device. Being powered by computer vision, human pose estimation and natural language processing algorithms, these technologies lead end-users through a number of workouts and give real-time feedback.

How Human Pose Estimation Works in Fitness Apps

In order to understand whether modern fitness apps can really help to perform physical exercises properly, let’s review how the human pose estimation-based apps work.

At the core of any human pose estimation application lies a pose estimation algorithm that receives an image of a person as an input and outputs the coordinates of the specific keypoints or landmarks on the human body (XY coords in 2D pose estimation or XYZ coords in 3D pose estimation). Modern pose estimation algorithms are almost exclusively based on convolutional neural networks with hourglass architecture or its variants (see the image below). Such a network consists of two major parts: a convolutional encoder that compresses the input image into the so-called latent representation and decoder that constructs N heatmaps from the latent representation where N is the number of searched keypoints.

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