Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment - Teipel, Stefan J.; Amaefule, Chimezie O.; Lüdtke, Stefan; Görß, Doreen; Faraza, Sofia; Bruhn, Sven; Kirste, Thomas (Frontiers in Psychology, 2022)
Objective: To determine whether gait and accelerometric features can predict disorientation events in young and old adults. Methods: Cognitively healthy younger (18 – 40 years, n = 25) and older (60 – 85 years, n = 28) participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab (GRAIL) system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real time data using Bayesian generalized mixed effect models. Accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines (SVM) and hierarchical Markov models (HMM). Results: Bayesian analysis revealed strong evidence for an effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors. Conclusion: Disorientation is reflected in spatiotemporal gait features and in the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events.