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

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 3; p. 919
• Main Authors: Mileti, Ilaria, Germanotta, Marco, Di Sipio, Enrica, Imbimbo, Isabella, Pacilli, Alessandra, Erra, Carmen, Petracca, Martina, Rossi, Stefano, Del Prete, Zaccaria, Bentivoglio, Anna Rita, Padua, Luca, Palermo, Eduardo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20-03-2018; MDPI
• Subjects: Foot; Gait; gait phases recognition; gait quality; Humans; Machine Learning; Monitoring; motor fluctuations; Parkinson Disease; Parkinson's disease; Partitioning; Patients; Quality; Quality assessment; Real time; Sensors; Walking; wearable sensor system; Wearable technology; gait phases recognition; machine learning; motor fluctuations; gait quality; Parkinson’s disease; wearable sensor system
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18030919

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.