Convolutional LSTM: a deep learning approach to predict shoulder joint reaction forces. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Mubarrat, S. T.; Chowdhury, S.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 26:Issue 1(2023)
• Page Start: 65
• Page End: 77
• Publication Date: 2023-01-02
• Subjects: Deep learning network -- AnyBody musculoskeletal modelling -- convolutional LSTM -- shoulder movement -- shoulder joint reaction forces; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2022.2045974
• Languages: English
• ISSNs: 1025-5842

Abstract

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.