Abstract
Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis.
Highlights
Falls are amongst the most serious and costly accidents in older adults
The four relevant gait parameters were calculated from all three systems for all participants and manually merged by the study personnel into an Excel file
This study investigated the validity of camera-based technologies for gait analysis in comparison to a well-established gait analysis system
Summary
Falls are amongst the most serious and costly accidents in older adults. Due to problems in defining and recording falls, prevalence and incidence data vary widely. Age-related changes in step parameters are associated with a decrease in gait symmetry and step length and an increase in step time and step width [5]. The risk of falling increases with a reduction in gait speed. Even a gait speed reduction of only 0.1 m/s is related to a 7% increase in the risk of falling. Studies have shown that Kinect is only a valid measuring instrument for some gait parameters [4]. Clark et al demonstrated limited validity of the Kinect system for complex gait parameters [11]. Two camera-based systems for measuring different gait parameters were validated against a gold standard for gait analysis
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