Detection of signs of Parkinson's disease using dynamical features via an indirect pointing device ⁎Thanks to the ParkEvolution project (Carnot STAR and Carnot Inria), and Inria North European associated team WeCare (team.inria.fr/valse/we-care-inria-north-european-associate-team/). Issue 2 (2020)

• Record Type: Journal Article
• Authors: Ushirobira, Rosane; Efimov, Denis; Casiez, Géry; Fernandez, Laure; Olsson, Fredrik; Medvedev, Alexander
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 16347
• Page End: 16352
• Publication Date: 2020
• Subjects: Quantification of physiological parameters for diagnosis -- treatment assessment -- Developments in measurement -- signal processing -- Control of physiological -- clinical variables; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.664
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, we study the problem of detecting early signs of Parkinson's disease during an indirect human-computer interaction via a computer mouse activated by a user. The experimental setup provides a signal determined by the screen pointer position. An appropriate choice of segments in the cursor position raw data provides a filtered signal from which a number of quantifiable criteria can be obtained. These dynamical features are derived based on control theory methods. Thanks to these indicators, a subsequent analysis allows the detection of users with tremor. Real-life data from patients with Parkinson's and healthy controls are used to illustrate our detection method.