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Convolutional Neural Network based Ground Reaction Forces and Center of Pressure Estimation During Stair Walking Using Multi-level Kinematics

Ground reaction forces (GRFs) and center of pressure trajectories (CoPs) are required for a comprehensive biomechanical analysis. They are also important outcome measures in sports sciences or clinical areas. GRFs and CoPs are usually measured by force plate, which is rarely equipped on staircases in laboratories. We present a one-dimensional convolutional neural network for estimating GRFs and CoPs during stair ascent and descent using multi-level of kinematics as input.

Categories:
Machine Learning
Biomedical and Health Sciences
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