From Simulation to Reality: Predicting Torque With Fatigue Onset via Transfer Learning.

Muscle fatigue impacts upper extremity function but is often overlooked in biomechanical models. The present work leveraged a transfer learning approach to improve torque predictions during fatiguing upper extremity movements. We developed two artificial neural networks to model sustained elbow flexion: one trained solely on recorded data (i.e., direct learning) and one pre-trained on simulated data and fine-tuned on recorded data (i.e., transfer learning). We simulated muscle activations and joint torques using a musculoskeletal model and a muscle fatigue model (n = 1,701 simulations). We also recorded static subject-specific features (e.g., anthropometric measurements) and dynamic muscle activations and torques during sustained elbow flexion in healthy young adults (n = 25 subjects). Using the simulated dataset, we pre-trained a long short-term memory neural network (LSTM) to regress fatiguing elbow flexion torque from muscle activations. We concatenated this pre-trained LSTM with a feedforward architecture, and fine-tuned the model on recorded muscle activations and static features to predict elbow flexion torques. We trained a similar architecture solely on the recorded data and compared each neural network's predictions on 5 leave-out subjects' data. The transfer learning model outperformed the direct learning model, as indicated by a decrease of 24.9% in their root-mean-square-errors (6.22 Nm and 8.28 Nm, respectively). The transfer learning model and direct learning model outperformed analogous musculoskeletal simulations, which consistently underpredicted elbow flexion torque. Our results suggest that transfer learning from simulated to recorded datasets can decrease reliance on assumptions inherent to biomechanical models and yield predictions robust to real-world conditions.