Fitness mobile apps are in trend as more people become health-conscious in this pandemic age. As per the Wellness Creatives report, the fitness industry grows by 8.7% every year, and customized fitness apps act as a catalyst in this growth. 

On one hand, feature-rich fitness apps bring digital transformation in fitness industry, and on the other hand, they can provide a personalized experience based on AI technology. 

We can mention several benefits of AI in fitness app development domain. One of them is human pose estimation, which is basically a computer vision-based technology to detect and process human posture. Human body modeling is the core part of this technology and it is related to the position and orientation of the human body. 

With the advent of the human pose estimation concept, fitness mobile apps can become more user-friendly and interactive. 

Let’s understand the working or mechanism of human pose estimation in AI-powered fitness apps. 

How Human Pose Estimation Works in AI-powered Fitness Apps

Advanced and AI-based fitness apps can help users to perform physical exercises properly in a personalized manner. A pose estimation algorithm receives an image of the user as input and shows the coordinates of the specific key points or landmarks on the human body. 

Modern pose estimation algorithms are exclusively powered by convolutional neural networks and hourglass architecture.

It also consists of two major parts- a convolutional encoder to compress the input image and decoder that builds N heatmaps from the latent representation. N is the number of searched key points in the human body in N heatmaps. 

A single heatmap is a one-channel image with the same resolution as the input. Each pixel has a probability of containing the values between 0 and 1 for the target keypoint. 

Usually, AI-based fitness mobile apps can be used efficiently through devices equipped with a camera. When it comes to human pose estimation in fitness app, the common algorithm follows four steps-

1. When the user starts the fitness app, the camera captures their movements during the exercise performance and records the video
2. The recorded video will split into individual frames that are processed in line with the human pose estimation model. Here, the key points on the user’s body are detected and the virtual model of ‘skeletons’ in the 2D or 3D. 
3. These virtual skeletons are analyzed through AI technology and the mistakes in the exercise pattern or technique are shown. 
4. The app user can receive the description of mistakes along with the recommendations to resolve them. 

These four steps seem simple, but while developing such apps, a fitness app development company faces many risks and challenges. 

Get more info : Human Pose Estimation in AI Fitness Apps