Regression Transfer Learning for the Prediction of Three-Dimensional Ground Reaction Forces and Joint Moments During Gait

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International Journal of Biomedical Engineering and Technology

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Clinical gait analysis is a useful tool for assessing a patient's walking conditions. Force platforms are gait analysis tools used to collect the ground reaction forces (GRFs); however, they are expensive and time-consuming. Therefore, this study focuses on the prediction of GRFs and joint moments without using force platforms. To address this problem, we proposed to combine deep learning methods with regression transfer learning (RTL). The inputs of the proposed method are joint angles and marker trajectories from a public dataset. Principal component analysis (PCA) has been used to reduce the data dimensionality to improve the computational time and prediction accuracy. A synthetic dataset has been generated to pre-train the deep learning method for transfer learning purpose. The experimental results indicate that the proposed transfer learning method increases the target domain's learning process and can successfully predict the average GRFs and joint moments with 97.44% and 96.56% accuracy, respectively.


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