An RNN-Ensemble approach for Real Time Human Pose Estimation from Sparse IMUs
Deepak Nagaraj, Erik Schake, Patrick Leiner, Dirk Werth · 2020
With recent advances in various hardware technologies, human motion capturing (MoCap) has gained importance in the fields such as computer vision, computer animation, gesture recognition in gaming, and most importantly in bio-mechanical analysis. In this direction, human motion is being captured using various kinds of sensors. Correspondingly, many model-based and data-based techniques have been developed in order to decode sensor readings into information understandable by a person. Given that the current technologies still lack applicability in real-world scenarios considering cost and ease of information gathering, leaves substantial room for improvement. This article focuses on the development of a novel machine learning based proof of concept for real-time human pose estimation using data collected from sparse inertial measurement units (IMU) system which is cost-effective and least intrusive in the scope of skilled crafts domain. Training diverse bi-directional recurrent neural networks (bi-RNN) with variable window size and building an ensemble of these models to estimate human pose in terms of human-joints' angles more accurately and robustly is discussed.