Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

TL;DR

This study introduces the PaHaW handwriting database and demonstrates that analyzing kinematic and pressure features in handwriting can effectively differentiate Parkinson's disease patients from healthy individuals with over 80% accuracy.

Contribution

The paper presents a new handwriting database for Parkinson's diagnosis and shows that pressure features are particularly effective for classification.

Findings

Support vector machine achieved 81.3% accuracy in PD detection.

Pressure features alone outperformed kinematic features in classification.

Handwriting analysis can reveal subtle PD-related motor impairments.

Abstract

Objective: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and Material: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). Results: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc = 81.3% (sensitivity Psen = 87.4% and specificity of Pspe = 80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc = 82.5% compared to Pacc = 75.4% using kinematic features. Conclusion: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.