Handwriting Feature

Handwriting feature analysis uses machine learning to extract meaningful information from handwriting samples, aiming to improve diagnosis of neurological disorders like Alzheimer's and Parkinson's disease, and to understand the developmental trajectory of handwriting skills. Current research employs various approaches, including dynamic system models to capture temporal aspects of handwriting, and deep learning architectures for feature extraction and classification, often leveraging kinematic and pressure data. This research holds significant potential for developing non-invasive diagnostic tools and assistive technologies, particularly for early detection and monitoring of neurodegenerative diseases and learning disabilities like dysgraphia.