Spontaneous Motor

Spontaneous motor activity, encompassing both naturally occurring movements and those generated by artificial systems, is a focus of research aiming to understand its underlying neural mechanisms and develop improved methods for its analysis and prediction. Current research utilizes diverse approaches, including deep learning models (e.g., graph convolutional networks, sensor fusion techniques) and novel training paradigms (e.g., pretraining with random noise), to analyze movement data from various sources (e.g., 3D video, multiple sensor modalities). These advancements hold significant promise for early detection of neurodevelopmental disorders in infants and for creating more natural and expressive human-robot interaction.