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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:

    Erscheinungsdatum :

    01.01.2018



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Effect of Parkinson's Disease on Gait Symmetry

    Fuyuan, L. / Jue, W. | British Library Online Contents | 2006


    Quantitative and qualitative gait assessments in Parkinson's disease patients

    Đurić-Jovičić, Milica D. / Jovičić, Nenad S. / Radovanović, Saša M. et al. | BASE | 2014

    Freier Zugriff

    Gait recognition using a few gait frames.

    Yao, L / Kusakunniran, W / Wu, Q et al. | BASE | 2021

    Freier Zugriff

    Markerless gait analysis vision system for real-time gait monitoring

    André, João Carlos Vieira Peixoto / Lopes, João Pedro Mendes / Palermo, Manuel Castro et al. | BASE | 2020

    Freier Zugriff

    Crossing virtual doors::A New Method to Study Gait Impairments and Freezing of Gait in Parkinson's Disease

    Gómez-Jordana Martín, L.I. / Stafford, J. / Peper, C.E. et al. | BASE | 2018

    Freier Zugriff