Research on Body movements
Fitness Uses
Animation
Engaging
Interactive installations
Single person pose estimation
Tensorflow.js
JavaScript
Flutter
One of the popular health & fitness chain of Singapore wanted to make their live exercise sessions through browser and mobile apps interactive and measureable. They wanted to bring in an energy meter which could detect body movement of their audience during their live coaching sessions. And this analysis provided to each of their health members at end of session.
We designed Module with Tensorflow.js and anyone with webcam equipped desktop or phone could use this. This module can be used to estimate multiple poses of a single person. In a security level, we made sure no pose data leaves user’s device. A lot of work went into abstracting away the complexities of the model and encapsulating functionality into easy-to-use methods. Atrous Convolution is used to enable the convolution filters in the subsequent layers to have a wider field of view.
Animation based Approach
Easily configured animation during movements used. Its ideal use case is for when there is only one person centered in an input video.
Keypoint Detection
All activities are real-time based. Module detects 17 key points to measure the movements.
Acknowledgement of Rapid Movements
User is notified for rapid movements for a longer time through interactive interface. This encourages user to be in line with coach.
Image Size Invariant
Module is designed to be image size invariant, which means it can predict pose positions in the same scale as the original image regardless of whether the scale of image changes.
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