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Medical & Biological Engineering & Computing

, Volume 54, Issue 1, pp 223–233 | Cite as

Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

  • Claas AhlrichsEmail author
  • Albert Samà
  • Michael Lawo
  • Joan Cabestany
  • Daniel Rodríguez-Martín
  • Carlos Pérez-López
  • Dean Sweeney
  • Leo R. Quinlan
  • Gearòid Ò Laighin
  • Timothy Counihan
  • Patrick Browne
  • Lewy Hadas
  • Gabriel Vainstein
  • Alberto Costa
  • Roberta Annicchiarico
  • Sheila Alcaine
  • Berta Mestre
  • Paola Quispe
  • Àngels Bayes
  • Alejandro Rodríguez-Molinero
Original Article

Abstract

Freezing of gait (FOG) is a common motor symptom of Parkinson’s disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier’s outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7 % accuracy and a geometric mean of 96.1 %. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90 % and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.

Keywords

Parkinson’s disease Freezing of Gait Machine learning Support vector machines 

Notes

Acknowledgments

This work has been performed in the framework of the FP7 project REMPARK ICT-287677, which is funded by the European Community. The author(s) would like to acknowledge the contributions of their colleagues from REMPARK Consortium (http://www.rempark.eu). We also like to thank all participants without whom this publication would not have been possible.

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Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • Claas Ahlrichs
    • 1
    Email author
  • Albert Samà
    • 3
  • Michael Lawo
    • 2
  • Joan Cabestany
    • 3
  • Daniel Rodríguez-Martín
    • 3
  • Carlos Pérez-López
    • 3
  • Dean Sweeney
    • 4
  • Leo R. Quinlan
    • 4
  • Gearòid Ò Laighin
    • 4
  • Timothy Counihan
    • 5
  • Patrick Browne
    • 5
  • Lewy Hadas
    • 6
  • Gabriel Vainstein
    • 6
  • Alberto Costa
    • 7
    • 9
  • Roberta Annicchiarico
    • 7
  • Sheila Alcaine
    • 8
  • Berta Mestre
    • 8
  • Paola Quispe
    • 8
  • Àngels Bayes
    • 8
  • Alejandro Rodríguez-Molinero
    • 4
  1. 1.neusta mobile solutions GmbH (NMS)BremenGermany
  2. 2.Institute for Artificial Intelligence (AGKI)University of BremenBremenGermany
  3. 3.Technical Research Centre for Dependency Care and Autonomous Living(CETpD)Universitat Politcnica de CatalunyaVilanova i la GeltrSpain
  4. 4.Electrical & Electronic Engineering DepartmentNUI GalwayGalwayIreland
  5. 5.School of MedicineNUI GalwayGalwayIreland
  6. 6.Maccabi Healthcare ServicesTel AvivIsrael
  7. 7.IRCCS Fondazione Santa LuciaRomeItaly
  8. 8.Unidad de Parkinson y trastornos del movimiento (UParkinson)BarcelonaSpain
  9. 9.Niccolò Cusano University of RomeRomeItaly

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