Measuring gait quality in Parkinson’s disease through real-time gait phase recognition

Anna Rita Bentivoglio, Luca Padua, Marco Germanotta, Isabella Imbimbo, Carmen Erra, Martina Petracca, Ilaria Mileti, Enrica Di Sipio, Alessandra Pacilli, Stefano Rossi, Zaccaria Del Prete, Eduardo Palermo

Risultato della ricerca: Contributo in rivistaArticolo in rivista

19 Citazioni (Scopus)

Abstract

Monitoring gait quality in daily activities through wearable sensors has the potential to improve medical assessment in Parkinson’s Disease (PD). In this study, four gait partitioning methods, two based on thresholds and two based on a machine learning approach, considering the four-phase model, were compared. The methods were tested on 26 PD patients, both in OFF and ON levodopa conditions, and 11 healthy subjects, during walking tasks. All subjects were equipped with inertial sensors placed on feet. Force resistive sensors were used to assess reference time sequence of gait phases. Goodness Index (G) was evaluated to assess accuracy in gait phases estimation. A novel synthetic index called Gait Phase Quality Index (GPQI) was proposed for gait quality assessment. Results revealed optimum performance (G < 0.25) for three tested methods and good performance (0.25 < G < 0.70) for one threshold method. The GPQI resulted significantly higher in PD patients than in healthy subjects, showing a moderate correlation with clinical scales score. Furthermore, in patients with severe gait impairment, GPQI was found higher in OFF than in ON state. Our results unveil the possibility of monitoring gait quality in PD through real-time gait partitioning based on wearable sensors.
Lingua originaleEnglish
pagine (da-a)1-16
Numero di pagine16
RivistaSensors
Volume18
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Electrical and Electronic Engineering
  • Gait phases recognition
  • Gait quality
  • Instrumentation
  • Machine learning
  • Motor fluctuations
  • Parkinson’s disease
  • Wearable sensor system

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