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Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning

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Abstract

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data’s temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Graphical abstract

Gait-related data are temporal-dependent, multidimensional, and highly heterogeneous. Therefore, we designed a multisource fusion recurrent neural network (MF-LSTM) structure and introduced a transfer learning strategy to predict knee contact force (KCF) during gait.

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Funding

This work was supported by “the National Natural Science Foundation of China (Nos. 52035012, 52005418, and 52275215) and Natural Science Foundation of Sichuan Province (No. 2022NSFSC1940)”. The authors gratefully acknowledge “ Grand Challenge Competition to Predict In Vivo Knee Loads” for releasing the experimental data.

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Jianjun Zou, Xiaogang Zhang, Yali Zhang & Zhongmin Jin

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Zhongmin Jin

  3. School of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK

    Zhongmin Jin

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  1. Jianjun Zou
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  2. Xiaogang Zhang
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Correspondence to Xiaogang Zhang.

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Zou, J., Zhang, X., Zhang, Y. et al. Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning. Med Biol Eng Comput 62, 1333–1346 (2024). https://doi.org/10.1007/s11517-023-03011-w

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