Estimation of 3D Ground Reaction Force and 2D Center of Pressure Using Deep Learning and Load Cells Across Various Gait Conditions.

ABSTRACT: Traditional force plate-based systems offer high measurement precision but are limited to laboratory settings, restricting their use in real-world environments. To address this, we propose a method for estimating a three-axis ground reaction force (GRF) and two-axis center of pressure (CoP) using a shoe embedded with three uniaxial load cells. The estimation was conducted under five gait conditions: straight walking, turning, uphill, downhill, and running. Data were collected from 40 healthy young adults. Four deep-learning models-Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Sequence-to-Sequence Long Short-Term Memory (Seq2Seq-LSTM), and Transformer-were evaluated. Among them, Seq2Seq-LSTM and CNN achieved the highest performance in predicting both GRF and CoP

SUBMITTER: Kim J 

PROVIDER: S-EPMC12157732 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature