Insights into motor control: predict muscle activity from upper limb kinematics with LSTM networks.

ABSTRACT: This study explores the relationship between upper limb kinematics and corresponding muscle activity, aiming to understand how predictive models can approximate motor control. We employ a Long Short-Term Memory (LSTM) network trained on kinematic end effector data to estimate muscle activity for eight muscles. The model exhibits strong predictive accuracy for new repetitions of known movements and generalizes to unseen movements, suggesting it captures underlying biomechanical principles rather than merely memorizing patterns. This generalization is particularly valuable for applications in rehabilitation and human-machine interaction, as it reduces the need for exhaustive datasets. To further investigate movement representation and learning, we analyze the impact of motion segmentation, h

SUBMITTER: Schmidt MD 

PROVIDER: S-EPMC12820307 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature