Next Article in Journal
Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor
Next Article in Special Issue
Detection of Hemiplegic Walking Using a Wearable Inertia Sensing Device
Previous Article in Journal
Arc-Induced Long Period Gratings from Standard to Polarization-Maintaining and Photonic Crystal Fibers
Previous Article in Special Issue
Motor Control Training for the Shoulder with Smart Garments
Open AccessArticle

Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition

1
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy
2
Fondazione Don Carlo Gnocchi Onlus, 20121 Milan, Italy
3
Department of Geriatrics, Neurosciences and Orhopaedics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
4
Department of Economics, Engineering, Society and Business Organization (DEIM), University of Tuscia, 01100 Viterbo, Italy
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(3), 919; https://doi.org/10.3390/s18030919
Received: 16 February 2018 / Revised: 10 March 2018 / Accepted: 19 March 2018 / Published: 20 March 2018
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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. View Full-Text
Keywords: gait quality; gait phases recognition; machine learning; Parkinson’s disease; motor fluctuations; wearable sensor system gait quality; gait phases recognition; machine learning; Parkinson’s disease; motor fluctuations; wearable sensor system
Show Figures

Figure 1

MDPI and ACS Style

Mileti, I.; Germanotta, M.; Di Sipio, E.; Imbimbo, I.; Pacilli, A.; Erra, C.; Petracca, M.; Rossi, S.; Del Prete, Z.; Bentivoglio, A.R.; Padua, L.; Palermo, E. Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition. Sensors 2018, 18, 919.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop