Exercise posture detection

In today’s fast-paced world, many people make physical fitness and mental well-being a priority. Regular exercise improves overall health. However, people often overlook correct posture during exercise, which can lead to injuries and ineffective workouts. To address this, posture detection systems have become valuable tools in the fitness industry.

Posture detection technology uses machine learning to identify improper posture during specific exercises. It analyses key body points and gives users feedback, so they keep proper form and get more from their workouts. Whether on a treadmill or doing squats, machine learning helps assess posture accurately across many exercise types. Our posture anomaly detection system builds on this idea, and we built it with Python.

However, existing posture detection systems focus on general posture correction. Our solution, in contrast, aims to find incorrect posture during different exercises. One primary challenge is adding exercise type recognition to the detection process. Our system handles this by letting users define exercise types and set rules for them.

For this, our model uses the Google Pose Landmarker library. It identifies key body points and marks incorrect posture positions. Moreover, it explains why the detected posture is incorrect, so users understand better and stay engaged.

This application helps users with their workout routines by giving posture detection and correction feedback. Users can personalise exercise types and rules based on their needs. It also integrates with existing fitness platforms and devices.

First, at its core, the solution is a posture analysis application that uses Google’s Pose Landmarker library to detect key points on the body. Then it checks these points against predefined rules for each exercise, which we store in a JSON file. It uses angle calculations and distance measurements between points to identify correct posture. If the posture is incorrect, the OpenCV library in Python highlights the problem areas and gives visual feedback to the user. This is how the system flags a posture anomaly.

The posture anomaly detection system is scalable and adaptable. We can add new exercises by adding exercise information and matching rules.

Finally, the application can run in Docker containers on a server. It has an API that receives images and exercise types and returns feedback. Read more about our AI product engineering work.