Dynamically enhanced static handwriting representation for Parkinson's disease detection

TL;DR

This paper introduces a novel static handwriting representation that incorporates dynamic information through synthetic enhancements, improving Parkinson's disease detection accuracy over traditional static or dynamic methods.

Contribution

It proposes a new static handwriting representation embedding dynamic features, evaluated with transfer learning and ensemble classifiers, outperforming existing static and dynamic approaches.

Findings

Outperforms state-of-the-art static and dynamic handwriting methods

Uses transfer learning and ensemble classifiers for improved accuracy

Demonstrates effectiveness on the PaHaW dataset

Abstract

Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of "dynamically enhanced" static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.