Gait analysis of older adults: Gait characteristics calculation and environmental factors

Abstract

Background: Gait characteristics are good indicators for physical health. Early detection improves clinical outcomes. The main influences on gait characteristics come from health factors, individual factors and environmental factors. While health and individual factors are widely researched, the environmental factors have been largely disregarded. This thesis aims to support the health monitoring research with a smartphone-based gait characteristics calculation and shine some light on environmental factors. Methods: GPS and inertial measurement unit (IMU) data is pre-processed with GPS noise filters, step detection and bout detection. This allows for the calculation of the gait characteristics gait speed, step length, step time and cadence. These characteristics are put in context with more intermediate characteristics and external data. Results: The GPS data was already filtered, so additional filtering did not yield better results. A high accuracy for step detection was found, with consistent undercounting. The calculated gait characteristics were higher than in other literature, but within a reasonable range. Few correlations were significant. The stop characteristics could be linked directly to most gait characteristics. The surface of asphalt could be linked to a reduced number of stops and stop time. Conclusion: The correlation between stops and gait characteristics is potentially great news. If stops can be directly linked to health, then a simple IMU would be sufficient for health monitoring. This would improve health monitoring in areas with degraded GPS signals, like inside buildings. However, many limitations were found that may be reduced with future research