Human Pose Estimation Using Depth-Wise Separable Convolutional Neural Networks
Anthony Tannoury, E M Choueiri, Rony Darazi · Zenodo (CERN European Organization for Nuclear Research) · 2022
When it comes to dynamic human pose estimation, the process known as "identifying human joints in an image or video and determining their position in space" is used. This is done so that the dynamic position of the human body can be more accurately estimated and evaluated. This goal can be achieved by applying various computer vision strategies used in a number of industries such as gaming, robotics training, and animation. In this article, we propose a method for dynamic human pose estimation using convolutional neural networks (CNN). This method will soon be used as a form of physical therapy rehabilitation that can be performed in a remote setting. By making an assessment of the patient's postures, the physical therapist can determine whether or not the patient is performing the assigned exercises correctly. With this method, the physiotherapist can correctly adapt the therapy sessions to the progress that the patient is making in the recovery process.