Detection of signs of Parkinson's disease using dynamical features via an indirect pointing device. 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.