On Mobile Pose Estimation Design and Implementation

Abstract

Human Pose Estimation (PE, tracking body pose on-the-go) is a computer vision-based technology that identifies and controls specific points on the human body.These points represent our joints and special points over the body determining the sizes, distances, angle of flexion, and type of the motion.Knowing this in a specific exercise is the basis of work for rehabilitation and physiotherapy, fitness and self-coaching, augmented reality, animation and gaming, robot management, surveillance and human activity analysis.Implementing such capabilities may use special suits or sensor arrays to achieve the best result, but massive use of PE is related to devices that many users own -namely smartphones, smartwatches, and earbuds.The body pose estimation system starts with capturing the initial data.In dealing with motion detection, it is necessary to analyze a sequence of images rather than a still photo.Different software modules are responsible for tracking 2D key points, creating a body representation, and converting it into a 3D space.Human pose estimation is a machine-learning technology, which means that we need data to train it.Since human pose estimation completes quite difficult tasks of detecting and recognizing multiple objects on the screen, they use neural networks as an engine of it.Human pose estimation projects can be quite complex and require expertise in a number of domains.They need compact tools of generative NN and transformers, the use of special Dynamic Time Warping, movement coding languages, recommenders and decision making.