title: Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease.
abstract: OBJECTIVE
  Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis.
  
  
  METHODS
  We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls.
  
  
  RESULTS
  By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy.
  
  
  CONCLUSIONS
  Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.
authors.affiliation: Brno University of Technology, Technicka 12, Brno, Czech Republic.
authors.forename: Peter
authors.initials: P
authors.lastname: Drotár
authors.affiliation: Brno University of Technology, Technicka 12, Brno, Czech Republic.
authors.forename: Jiří
authors.initials: J
authors.lastname: Mekyska
authors.affiliation: First Department of Neurology, Masaryk University and St. Anne's Hospital, Pekarska 664, 656 91 Brno, Czech Republic. Electronic address: irena.rektorova@fnusa.cz.
authors.forename: Irena
authors.initials: I
authors.lastname: Rektorová
authors.affiliation: First Department of Neurology, Masaryk University and St. Anne's Hospital, Pekarska 664, 656 91 Brno, Czech Republic.
authors.forename: Lucia
authors.initials: L
authors.lastname: Masarová
authors.affiliation: Brno University of Technology, Technicka 12, Brno, Czech Republic.
authors.forename: Zdenek
authors.initials: Z
authors.lastname: Smékal
authors.affiliation: Tecnocampus, Av. Ernest Lluch, 32, 08302 Mataro, Barcelona, Spain.
authors.forename: Marcos
authors.initials: M
authors.lastname: Faundez-Zanuy
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D020000:Decision Support Systems, Clinical; D003937:Diagnosis, Differential; D005260:Female; D006225:Hand / Q000502:physiology*; D006236:Handwriting; D006801:Humans; D008297:Male; D008875:Middle Aged; D009048:Motor Skills; D009068:Movement; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: Decision support systems; Disease classification; Handwriting; In-air movement; Micrographia; Parkinson's disease