Advanced Parkinson's Disease Dysgraphia Analysis Based on Fractional Derivatives of Online Handwriting
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | 2018 10TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT 2018): EMERGING TECHNOLOGIES FOR CONNECTED SOCIETY |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/ICUMT.2018.8631265 |
| Keywords | kinematic analysis; fractal calculus; fractional derivative; online handwriting; Parkinson's disease; Parkinson's disease dysgraphia |
| Description | Parkinson's disease (PD) is one of the most frequent neurodegenerative disorder with progressive decline in several motor and non-motor skills. Due to time-consuming and partially subjective conventional PD diagnosis, several more effective approaches based on signal processing and machine learning, e.g. online handwriting analysis, have been proposed. This paper introduces a new methodology of PD dysgraphia analysis based on fractional derivatives applied in PD handwriting quantification. The proposed methodology was evaluated on a database that consists 33 PD patients and 36 healthy controls who performed several handwriting tasks. Employing random forests classifier in combination with 5 kinematic features based on fractionalorder derivatives we reached 90% classification accuracy, 89% sensitivity, and 91% specificity. In comparison with the results of other related works dealing with the same database, the proposed approach brings improvements in PD dysgraphia diagnosis and confirms the impact of fractional derivatives in kinematic analysis. |
| Related projects: |