Ground reaction force
Gait Event Detection (GED) plays a pivotal role in understanding human locomotion, with applications spanning rehabilitation, prosthesis design, sports science, and biomechanics. Accurate identification of key gait events—such as Heel Strike (HS), Loading Response (LS), Mid-Stance (MS), and Heel Off (HO)—during the stance phase of the gait cycle is essential for analyzing movement patterns, diagnosing gait abnormalities, and developing assistive technologies.
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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.
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