Personalized Myoelectric Control for Upper-Limb Exoskeletons Through Meta-Learning: A Few-Shot Learning Approach

Personalization in the myoelectric control of robotic exoskeletons is crucial to ensuring accurate interpretation and adaptation to the unique muscle activity patterns and movement intentions of each user. This approach minimizes the risk of incorrect or excessive force application, significantly reducing the likelihood of user discomfort or injury during operation. This study introduces a model-agnostic meta-learning approach for personalizing a soft upper-limb exoskeleton in industrial settings. The framework incorporates an attention-based CNN-LSTM model that predicts future angular positions of the robot using EMG and IMU signals. The MAML framework demonstrates significant adaptability and personalization, efficiently predicting future angular positions with minimal data, approximately 20-25 seconds per task. This approach effectively reduces the necessity for extensive retraining with new users or in new environments by 50%, showcasing real-time task adaptation capabilities. Our findings confirmed a reduced human effort of nearly 13% in load-bearing tasks. Also, the results show that the exerted torque from the exoskeleton was 24% higher while maintaining higher accuracy. A comparison with other deep learning models further emphasizes the enhanced adaptability and accuracy offered by the meta-learning approach.