A model-free technique based on computer vision and sEMG for classification in Parkinson's disease by using computer-assisted handwriting analysis.

Highlights • Computer-assisted handwriting analysis applied in Parkinson's disease research. • Model-free technique for differentiating Parkinsonian patients from healthy subjects. • Finding most representative features and best writing patterns for Parkinson research. • Comparison of AI-based classification approaches for handwriting analysis. • Role of muscular activities on handwriting analysis applied to Parkinson research. Abstract Patients suffering from Parkinson's disease are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose a model-free technique for differentiating Parkinson's disease patients from healthy subjects by using a handwriting analysis tool based on computer vision and surface ElectroMyoGraphy (sEMG) signal-processing techniques and an Artificial Intelligence-based classifier. Experimental tests have been conducted with both healthy and Parkinson's Disease patients using the proposed technique to address some specific research scientific questions regarding most representative features, best writing patterns, best AI-based classification approach between ANN optimal topology and SVM approaches in terms of both accuracy and repeatability of the results. Finally, the obtained results are reported and discussed to infer some important properties on writing patterns, classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research. [ABSTRACT FROM AUTHOR]