Real-Time Detection of Spatial Disorientation in Persons with Mild Cognitive Impairment and Dementia.
Accelerometer
Assistive technology
Dementia
Sensors
Spatial disorientation
Journal
Gerontology
ISSN: 1423-0003
Titre abrégé: Gerontology
Pays: Switzerland
ID NLM: 7601655
Informations de publication
Date de publication:
2020
2020
Historique:
received:
26
08
2018
accepted:
15
05
2019
pubmed:
31
7
2019
medline:
4
8
2020
entrez:
31
7
2019
Statut:
ppublish
Résumé
Detecting manifestations of spatial disorientation in real time is a key requirement for adaptive assistive navigation systems for people with dementia. To identify predictive patterns of spatial disorientation in cognitively impaired people during unconstrained locomotion behavior in an urban environment. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 31362286
pii: 000500971
doi: 10.1159/000500971
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
85-94Informations de copyright
© 2019 S. Karger AG, Basel.