Week 7: Pose Analysis with Angular Metrics

Welcome back to my blog tracking this journey in developing the virtual personal trainer app! This week, we made a pivotal shift in our approach to analyzing and normalizing human poses from scaling landmarks to a reference model, to utilizing angular measurements between joints. This change is designed to enhance the efficiency and accuracy of our app, making it more beneficial for users seeking real-time exercise feedback. Let’s dive into the details of this transformation and its impact on our project.

Moving Away From Landmark Scaling

Previously, our method for normalizing poses involved scaling each landmark to match a predefined reference model. While this is effective in ensuring a uniform comparison across different body sizes and shapes, this approach was computationally expensive andsometimes resulted in loss of posture details. Scaling each landmark by a different value after translation and proceeding to compare with a reference required unnecessary steps, especially in real-time applications. Additionally, leg movement and position during the squat is much more relevant than that of the upper body, and there are also various different styles of squatting. Therefore, in an analysis only considering position, some “correct” squats might be marked as incorrect.

Embracing Angular Analysis

To address these challenges, I shifted the approach to analyzing the angles formed between various joints. This method relies on the geometric relationships that are less resource-intensive to compute while also providing a better analysis. To summarize the benefits of this approach:

Reduced Computational Load: Calculating angles between joints is significantly less demanding on processing power, facilitating smoother and faster analysis, especially in real-time scenarios.

Preservation of Posture Details: Unlike scaling, which can result in data loss, angular analysis retains the unique aspects of each user’s movements, enhancing the personalized feedback our app provides. This will make it easier to cater to users with various body types.

Adaptability Across Different Exercises: Angular measurements maintain versatility in analyzing a wide range of exercises, keeping it simple to adjust the app for various fitness routines

Increased accuracy: This implementation of angular analysis involves calculating the angles between key joints such as elbows, shoulders, knees, and hips during exercises. These angles will be compared against optimal ranges derived from kinesiological standards to assess the correctness of each pose, giving more reliable and accurate feedback

Thank you for your continued support and interest in this project!