Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer’s disease patients

Ghosh, Abhirup, Puthusseryppady, Vaisakh, Chan, Dennis, Mascolo, Cecilia and Hornberger, Michael (2022) Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer’s disease patients. Scientific Reports, 12 (1). ISSN 2045-2322

PDF (Published manuscript) - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview


Impairment of navigation is one of the earliest symptoms of Alzheimer’s disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value 10−7), and distance from home (p-value 10−14). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.

Item Type: Article
Additional Information: Funding Information: Abhirup Ghosh, Dennis Chan, and Cecilia Mascolo acknowledge Wellcome Trust (Grant number 213939). Vaisakh Puthusseryppady and Michael Hornberger acknowledge Earle & Stuart Charitable Trust and the Faculty of Medicine and Health Sciences, University of East Anglia (Grant number R205319).
Faculty \ School: Faculty of Medicine and Health Sciences > School of Health Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 01 Mar 2022 14:30
Last Modified: 13 May 2022 00:22
DOI: 10.1038/s41598-022-06899-w

Actions (login required)

View Item View Item