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Computer Vision and EMG-Based Handwriting Analysis for Classification in Parkinson’s Disease

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

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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 ElectroMyoGraphy (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.

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Acknowledgments

This work has been funded from the FutureInResearch program of the Regione Puglia - project n. JTFWZV0 ABIOSAN - Advanced BIOmetric analysiS Against Neuromuscular disease.

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Authors and Affiliations

  1. Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Orabona 4, 70126, Bari, Italy

    Claudio Loconsole, Antonio Brunetti, Joseph Trotta, Angelo Schiavone & Vitoantonio Bevilacqua

  2. Department of Mechanics, Mathematics and Management (DMMM), Polytechnic University of Bari, Viale Japigia 182, 70126, Bari, Italy

    Gianpaolo Francesco Trotta

  3. Medica Sud s.r.l., Viale della Resistenza n. 82, Bari, BA, Italy

    Sabina Ilaria Tatò & Giacomo Losavio

Authors
  1. Claudio Loconsole
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  2. Gianpaolo Francesco Trotta
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  3. Antonio Brunetti
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  4. Joseph Trotta
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  5. Angelo Schiavone
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  6. Sabina Ilaria Tatò
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  7. Giacomo Losavio
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  8. Vitoantonio Bevilacqua
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Correspondence to Claudio Loconsole .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Loconsole, C. et al. (2017). Computer Vision and EMG-Based Handwriting Analysis for Classification in Parkinson’s Disease. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_43

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

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