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

S. T. Mubarrat, S. Chowdhury

Research output: Contribution to journalArticlepeer-review

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.

Keywords

  • AnyBody musculoskeletal modelling
  • Deep learning network
  • convolutional LSTM
  • shoulder joint reaction forces
  • shoulder movement

Fingerprint

Dive into the research topics of 'Convolutional LSTM: a deep learning approach to predict shoulder joint reaction forces'. Together they form a unique fingerprint.

Cite this