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

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.

Keywords: Parkinson’s disease; gait phases recognition; gait quality; machine learning; motor fluctuations; wearable sensor system.

Figure 1

Sensors positioning on subject. (a) Position of IMU on participant’s foot; (b) position of footswitches under participant’s foot.

Figure 2

(a,d,g) Mean and Standard Error of GPQI of patients in both condition (OFF, ON) and control group (CG); (b,e,h) ROC analysis of GPQI for OFF vs CG and ON vs CG; (c,f,i) ROC analysis of GPQI for OFF vs ON. Each chart type is reported for three subgroups, G_0–3, G_0–1 and G_2–3, respectively.