Handwriting analysis represents an important research area in different fields. From forensic science to graphology, the automatic dynamic and static analyses of handwriting tasks allow researchers to attribute the paternity of a signature to a specific person or to infer medical and psychological patients' conditions. An emerging research field for exploiting handwriting analysis results is the one related to Neurodegenerative Diseases (NDs). Patients suffering from a ND are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose an approach for differentiating Parkinson's disease patients from healthy subjects using a handwriting analysis tool based on a limited number of features extracted by means of both computer vision and Electro-MyoGraphy (EMG) signal-processing techniques and processed using an Artificial Neural Network-based classifier. Finally, we report and discuss the results of an experimental test conducted with both healthy and Parkinson's Disease patients using the proposed approach.

Computer vision and EMG-based handwriting analysis for classification in parkinson's disease

Loconsole, Claudio;Trotta, Gianpaolo Francesco;Brunetti, Antonio;Bevilacqua, Vitoantonio
2017

Abstract

Handwriting analysis represents an important research area in different fields. From forensic science to graphology, the automatic dynamic and static analyses of handwriting tasks allow researchers to attribute the paternity of a signature to a specific person or to infer medical and psychological patients' conditions. An emerging research field for exploiting handwriting analysis results is the one related to Neurodegenerative Diseases (NDs). Patients suffering from a ND are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose an approach for differentiating Parkinson's disease patients from healthy subjects using a handwriting analysis tool based on a limited number of features extracted by means of both computer vision and Electro-MyoGraphy (EMG) signal-processing techniques and processed using an Artificial Neural Network-based classifier. Finally, we report and discuss the results of an experimental test conducted with both healthy and Parkinson's Disease patients using the proposed approach.
Intelligent Computing Theories and Application: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017. Proceedings, part II
978-3-319-63311-4
978-3-319-63312-1
Springer
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/112311
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