Deep learning for freezing of gait detection in Parkinson's disease patients in their homes using a waist-worn inertial measurement unit

Julià Camps, Albert Samà, Mario Martín, Daniel Rodríguez-Martín, Carlos Pérez-López, Joan M. Moreno Arostegui, Joan Cabestany, Andreu Català, Sheila Alcaine, Berta Mestre, Anna Prats, Maria C. Crespo-Maraver, Timothy J. Counihan, Patrick Browne, Leo R. Quinlan, Gearóid Laighin, Dean Sweeney, Hadas Lewy, Gabriel Vainstein, Alberto CostaRoberta Annicchiarico, Àngels Bayés, Alejandro Rodríguez-Molinero

Research output: Contribution to journalArticlepeer-review

Abstract

Among Parkinson's disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient's condition and the symptom's characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.

Original languageEnglish
Pages (from-to)119-131
Number of pages13
JournalKnowledge-Based Systems
Volume139
DOIs
Publication statusE-pub ahead of print - Oct 16 2017

Keywords

  • Deep learning
  • Freezing of gait
  • Parkinson's disease
  • Signal processing
  • Wearable device

ASJC Scopus subject areas

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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