Dynamically enhanced static handwriting representation for Parkinson's disease detection

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.

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning
• Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
• A comparative study of in-air trajectories at short and long distances in online handwriting

Cited by in corpus (4)

• Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health
• Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs
• Ensembling complex network 'perspectives' for mild cognitive impairment detection with artificial neural networks
• Writing Order Recovery in Complex and Long Static Handwriting