Real_Time_Human_Pose_Detection_and_Guidance_on_Low_End_Devices

• This work proposes a light network for user activity guidance (such as yoga) in real time on computationally low-end CPUs.

  • It is built on top of Lightweight OpenPose by Daniil Osokin.
    

• The trained network is used to build a system that guides a user to learn a certain yoga by breaking it into states and providing corrective instructions throughout the activity.

• The trained lighter network is capable of retaining enough information to correctly identify different yoga poses while reducing the inference time by up to 4 folds.

• This is a collaborative work by Aashish Adhikari, Dhruv Jawalkar, and Sudha Ravi Javvadi.