title: Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease.
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
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Telecommunications, Brno University of Technology, Technická 12, 61200 Brno, Czech Republic.
authors.forename: Peter
authors.initials: P
authors.lastname: Drotár
authors.affiliation: Department of Telecommunications, Brno University of Technology, Technická 12, 61200 Brno, Czech Republic.
authors.forename: Jiří
authors.initials: J
authors.lastname: Mekyska
authors.affiliation: First Department of Neurology, Faculty of Medicine, St. Anns University Hospital, Pekarska 664, 66591 Brno, Czech Republic. Electronic address: rektorova@fnusa.cz.
authors.forename: Irena
authors.initials: I
authors.lastname: Rektorová
authors.affiliation: First Department of Neurology, Faculty of Medicine, St. Anns University Hospital, Pekarska 664, 66591 Brno, Czech Republic.
authors.forename: Lucia
authors.initials: L
authors.lastname: Masarová
authors.affiliation: Department of Telecommunications, Brno University of Technology, Technická 12, 61200 Brno, Czech Republic.
authors.forename: Zdeněk
authors.initials: Z
authors.lastname: Smékal
authors.affiliation: Signal Processing Group, Tecnocampus, Escola Universitaria Politecnica de Mataro, Avda. Ernest Llunch 32, 08302 Mataro, Spain.
authors.forename: Marcos
authors.initials: M
authors.lastname: Faundez-Zanuy
mesh_terms: D000368:Aged; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D003937:Diagnosis, Differential; D006236:Handwriting; D006801:Humans; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis*; D011312:Pressure; D060388:Support Vector Machine
keywords: Decision support system; Handwriting database; Handwriting pressure; PD dysgraphia; Parkinson's disease; Support vector machine classifier