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

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Ghosh, Abhirup 
Puthusseryppady, Vaisakh 
Chan, Dennis 
Mascolo, Cecilia 
Hornberger, Michael 


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 [Formula: see text]), and distance from home (p-value [Formula: see text]). 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.



Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Machine Learning, Male, Middle Aged, Neuropsychological Tests, Spatial Behavior, Spatial Navigation, Spatio-Temporal Analysis

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Springer Science and Business Media LLC
Wellcome Trust (213939/Z/18/Z)