Convolutional LSTM: a deep learning approach to predict shoulder joint reaction forces

S. T. Mubarrat and S. Chowdhury

Computer Methods in Biomechanics and Biomedical Engineering, 2023, vol. 26, issue 1, 65-77

Abstract: We developed a Convolutional LSTM (ConvLSTM) network to predict shoulder joint reaction forces using 3D shoulder kinematics data containing 30 different shoulder activities from eight human subjects. We considered simulation outcomes from the AnyBody musculoskeletal model as the baseline force dataset to validate ConvLSTM model predictions. Results showed a good correlation (>80% accuracy, r ≥ 0.82) between ConvLSTM predicted and AnyBody estimated force values, the generalization of the developed model for novel task type (p-value = 0.07 ∼ 0.33), and a better prediction accuracy for the ConvLSTM model than conventional CNN and LSTM models.

Date: 2023
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DOI: 10.1080/10255842.2022.2045974

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