ABSTRACT
BACKGROUND: Detecting manifestations of spatial disorientation in real time is a key requirement for adaptive assistive navigation systems for people with dementia. OBJECTIVE: To identify predictive patterns of spatial disorientation in cognitively impaired people during unconstrained locomotion behavior in an urban environment. METHODS: Accelerometric data and GPS records were gathered during a wayfinding task along a route of about 1 km in 15 people with amnestic mild cognitive impairment or clinically probable Alzheimer's disease dementia (13 completers). We calculated a set of 48 statistical features for each 10-s segment of the acceleration sensor signal to characterize the physical motion. We used different classifiers with the wrapper method and leave-one-out cross-validation for feature selection and for determining accuracy of disorientation detection. RESULTS: Linear discriminant analysis using three features showed the best classification results, with a cross-validated ROC AUC of 0.75, detecting 65% of all scenes of spatial disorientation in real time. Consideration of an additional feature that informed about a person's distance to the next traffic junction did not provide an additional information gain. CONCLUSIONS: Accelerometric data are able to capture the uniformity and activity of a person's walking, which are identified as the most informative locomotion features of spatially disoriented behavior. This serves as an important basis for real-time navigation assistance. To improve the required accuracy of real-time disorientation prediction, as a next step we will analyze whether location-based behavior is able to inform about person-centered habitual factors of orientation.
