GAIT ANALYSIS WITH TRINOCULAR COMPUTER VISION USING DEEP LEARNING

The recent years' progress in deep learning (DL) technology has resulted in convolutional neural networks (CNNs) capable of producing fast and accurate results, with minimal data preprocessing. Currently, gait analysis (GA) is attracting the attention of the field of deep learning due to its seamless integration applicability. Our approach focuses on CNN-based GA application on healthcare and orthopaedics. Using CNNs and visual fiducial systems for recognition and 3D mesh reconstruction of the human form and the floor respectively, we can virtually recreate the human-floor interaction, which can be particularly useful in the study of gait dynamics, through the per-frame gait phase classification. However, the current state-of-the-art (SOTA) is that most CNN mesh reconstruction software produces a mesh from a monocular input. The use of photogrammetry could alternatively be implemented, but would require multiple cameras and expensive equipment. Our approach aims to create a refined mesh obtained from trinocular footage, along with an interactive and easy-to-use interface.