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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
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 LOKI - Technology and knowledge for interaction
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189, Inria Lille - Nord Europe
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.
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https://hal.inria.fr/hal-02887913
Contributor : Rosane Ushirobira <>
Submitted on : Thursday, July 2, 2020 - 3:45:11 PM
Last modification on : Friday, July 3, 2020 - 3:41:41 AM

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  • HAL Id : hal-02887913, version 1

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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⟩

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