Abstract
Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.
Highlights
Parkinson’s disease (PD) is currently the world’s fastest-growing neurological disorder and characterized by motor and non-motor symptoms that worsen with disease progression [1]
2.3.ThSetgaatiitsptaircaamleAternreaflleycstiinsg bradykinesia and short steps measured by mobile sensor-based gait analysis system (MGL), MGL
The results showed that PD patients walked with a significantly reduced gait speed and stride length, and stride time increased by around 11% compared to healthy controls
Summary
Parkinson’s disease (PD) is currently the world’s fastest-growing neurological disorder and characterized by motor and non-motor symptoms that worsen with disease progression [1]. Gait impairment in PD is often characterized by short steps and a shuffling gait resulting in an increased risk of falling [2]. To determine the severity of gait disorders, an early and objective gait assessment is important [4,5]. Special high-frequency cameras calculate trajectories of these markers and produce quantified, reliable, and accurate results over short-distance walking tests. As data acquisition is time consuming, expensive, and can be performed by specialized personnel solely, a three-dimensional gait analysis using motion capture systems still requires enormous effort and is rarely practicable to use in a home-monitoring setting [4]
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