Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications
New paper Kolaghassi, Rania, Gianluca Marcelli, and Konstantinos Sirlantzis. 2023. "Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications" Sensors 23, no. 12: 5687. https://doi.org/10.3390/s23125687 Effect of Gait Speed on Trajectory Prediction Using Deep Learning Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications Sensors DOI https://doi.org/10.3390/s23125687 Abstract Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5...