Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer’s disease patients
Open accessAbstract
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 $${10}^{-7}$$ 10 - 7 ), and distance from home (p-value $${10}^{-14}$$ 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.
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A patient's mobility patterns differ when accompanied by carers.
“A patient’s mobility patterns differ when accompanied by carers.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [115]
Alzheimer's patients have significantly different outdoor mobility behaviour than control population even in early stage: patients explore spatially less compared to controls.
“Further it has been shown that Alzheimer’s patients have significantly different outdoor mobility behaviour than control population even in early stage [115]: patients explore spatially less compared to controls.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [115]
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- Authors
- Ghosh, A., Puthusseryppady, V., Chan, D., Mascolo, C., & Hornberger, M.
- Affiliations
- University of Cambridge (GB)
- University of East Anglia (GB)
- University College London (GB)
- Corresponding author
- Hornberger, Michael
- Venue
- Scientific Reports
- Published
- 2022-02-24
- Volume
- 12
- Issue
- 1
- Pages
- 3160-3160
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8873255
- OpenAlex
- W4213459059
- Cited by
- 39
- References
- 36
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 12 · 2023: 7 · 2022: 6
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Funders
- Wellcome Trust (213939)
- the Earle & Stuart Charitable Trust and the Faculty of Medicine and Health Sciences, University of East Anglia (R205319)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Alzheimer's disease, spatial navigation, GPS tracking, ecological validity, digital biomarkers, movement patterns, spatio-temporal features, graph analysis, wearable devices, early diagnosis, stop duration
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Male, Middle Aged, Neuropsychological Tests, Spatial Behavior, Spatio-Temporal Analysis, Spatial Navigation
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