Machine Learning Approaches For Motor Learning: A Short Review
This is an incremental review that identifies and categorizes adaptation methods for researchers and practitioners in motor learning and human movement modeling.
The paper reviews existing machine learning models for motor learning, focusing on their adaptation capabilities to handle motor variability and differentiate between new movements and variations of known ones, and discusses challenges for applying these models in motor learning support systems.
Machine learning approaches have seen considerable applications in human movement modeling, but remain limited for motor learning. Motor learning requires accounting for motor variability, and poses new challenges as the algorithms need to be able to differentiate between new movements and variation of known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.