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.
Highlights
In the last decade, increasing attention has been paid to novel methodologies for the real-time gait phase detection [1,2]
The purpose of this study is twofold: (i) comparing different gait partitioning methodologies involving threshold methods and machine-learning approaches for the estimation of the four gait phases in patients with Parkinson’s Disease (PD) both in ON and OFF levodopa conditions; and (ii) validating a novel synthetic index, the Gait Phases Quality Index (GPQI), for gait quality monitoring through gait partitioning in PD
In this study we introduced a novel index to synthetically quantify the quality of gait thought gait phases
Summary
In the last decade, increasing attention has been paid to novel methodologies for the real-time gait phase detection [1,2]. Some patients with advanced PD experience motor fluctuations which cause alternation between higher (ON state) and lower (OFF state) levels of gait quality, due to the long-term levodopa syndrome [8]. Common motor fluctuations are [8]: wearing-off effect, i.e., periods in which patients experience motor impairments earlier the end of dose due to a loss of treatment efficacy, and peak-dyskinesia, i.e., involuntary movements related to a (subjective) over-dose levodopa plasma level. Such motor fluctuations cause a sensible deterioration of patient’s quality of life. Motor fluctuations lead to fatigue and exhaustion, increasing the risk of fall [10], social withdrawal and isolation and consequent frustration and depression [10]
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