Movement Analysis
Movement analysis focuses on objectively quantifying and interpreting human and animal motion, aiming to understand behavior, diagnose conditions, and improve performance. Current research emphasizes developing robust and adaptable methods for capturing and analyzing movement data, including the use of diverse sensor technologies and advanced machine learning models like transformers and convolutional neural networks, often applied to state-space models for trajectory reconstruction and prediction. These advancements have significant implications for healthcare, particularly in early diagnosis of neurological disorders like Alzheimer's and cerebral palsy, and sports science, enabling detailed performance analysis and personalized training strategies.
Papers
Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent
Yi Xu, Yun Fu
A Machine Learning Approach to Analyze the Effects of Alzheimer's Disease on Handwriting through Lognormal Features
Tiziana D'Alessandro, Cristina Carmona-Duarte, Claudio De Stefano, Moises Diaz, Miguel A. Ferrer, Francesco Fontanella