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

Area of Science:

• Biomechanics
• Machine Learning
• Musculoskeletal Modeling

Background:

• Accurate prediction of shoulder joint reaction forces is crucial for understanding shoulder pathologies and designing effective interventions.
• Existing methods for estimating these forces often rely on complex musculoskeletal models or invasive measurements.

Purpose of the Study:

• To develop and validate a novel deep learning model, Convolutional LSTM (ConvLSTM), for predicting shoulder joint reaction forces.
• To compare the predictive accuracy of the ConvLSTM model against conventional deep learning approaches and established musculoskeletal modeling software.

Main Methods:

• A ConvLSTM network was trained using 3D shoulder kinematics data from eight subjects performing 30 distinct activities.
• The AnyBody musculoskeletal model's simulation outcomes served as the ground truth for validating the ConvLSTM predictions.
• Model performance was evaluated based on prediction accuracy and its ability to generalize to unseen tasks.

Main Results:

• The ConvLSTM model demonstrated a high correlation with AnyBody-estimated forces, achieving over 80% accuracy (r ≥ 0.82).
• The model showed good generalization capabilities for novel shoulder activities (p-value = 0.07–0.33).
• ConvLSTM outperformed conventional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models in prediction accuracy.

Conclusions:

• The developed ConvLSTM network offers a highly accurate and efficient method for predicting shoulder joint reaction forces from kinematic data.
• This AI-driven approach holds potential for non-invasive biomechanical analysis and clinical applications in shoulder joint assessment.
• The ConvLSTM model represents a significant advancement over traditional methods, providing a robust tool for research and practice.