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

Rosane Ushirobira (1) , Denis Efimov (1) , Géry Casiez (2) , Laure Fernandez (3) , Fredrik Olsson (4) , Alexander Medvedev (4)

1 VALSE - Finite-time control and estimation for distributed systems
2 LOKI - Technology and knowledge for interaction
3 ISM - Institut des Sciences du Mouvement Etienne Jules Marey
4 DIT-UPPSALA - Department of Information Technology

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.

Mots clés: Quantification of physiological parameters for diagnosis and treatment assessment. Developments in measurement, signal processing. Control of physiological and clinical variables.

Domaines: Automatique / Robotique

Rosane Ushirobira, Denis Efimov, Géry Casiez, Laure Fernandez, Fredrik Olsson, et al.. Detection of signs of Parkinson's disease using dynamical features via an indirect pointing device. IFAC 2020 - 21st IFAC World Congress, Jul 2020, Berlin, Germany. ⟨hal-02887913⟩