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Conference Papers Year : 2020

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

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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Dates and versions

hal-02887913 , version 1 (02-07-2020)

Identifiers

  • HAL Id : hal-02887913 , version 1

Cite

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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