Cited by 179Context-Aware Activity Recognition SystemsBalance, Gait, and Falls PreventionArtificial Intelligence in Healthcare

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PD Disease State Assessment in Naturalistic Environments Using Deep Learning

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Hammerla, Nils, Fisher, James, Andras, Peter, Rochester, Lynn, Walker, Richard, Ploetz, Thomas·2015·Proceedings of the AAAI Conference on Artificial Intelligence

DOI 10.1609/aaai.v29i1.9484

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Abstract

Management of Parkinson's Disease (PD) could be improved significantly if reliable, objective information about fluctuations in disease severity can be obtained in ecologically valid surroundings such as the private home. Although automatic assessment in PD has been studied extensively, so far no approach has been devised that is useful for clinical practice. Analysis approaches common for the field lack the capability of exploiting data from realistic environments, which represents a major barrier towards practical assessment systems. The very unreliable and infrequent labelling of ambiguous, low resolution movement data collected in such environments represents a very challenging analysis setting, where advances would have significant societal impact in our ageing population. In this work we propose an assessment system that abides practical usability constraints and applies deep learning to differentiate disease state in data collected in naturalistic settings. Based on a large data-set collected from 34 people with PD we illustrate that deep learning outperforms other approaches in generalisation performance, despite the unreliable labelling characteristic for this problem setting, and how such systems could improve current clinical practice.

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Authors: Hammerla, N., Fisher, J., Andras, P., Rochester, L., Walker, R., & Ploetz, T.
Affiliations: Newcastle University (GB)
Northumbria Healthcare NHS Foundation Trust (GB)
ORCID: Rochester, Lynn
Walker, Richard
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Published: 2015-02-18
Volume: 29
Issue: 1
ISSN: 2159-5399 (ISSN-L)
2374-3468
DOI: 10.1609/aaai.v29i1.9484
OpenAlex: W2142817672
Cited by: 179
References: 29
Citations by year: 2026: 1 · 2025: 5 · 2024: 8 · 2023: 15 · 2022: 7 · 2021: 27 · 2020: 22 · 2019: 33 · 2018: 25 · 2017: 15 · 2016: 14 · 2015: 7
FWCI: 8.34
Type: conference paper
Language: en
Open access: diamond
doi.org
Locations: Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
Keele Research Repository (Keele University) (submittedVersion, cc-by)
AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
Research Output (Edinburgh Napier University) (submittedVersion)
Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion)
Newcastle University ePrints (Newcastle Univesity)
Funders: Research Councils UK
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: Parkinson's disease, naturalistic settings, home monitoring, deep learning, movement data, clinical practice, population aging

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PD Disease State Assessment in Naturalistic Environments Using Deep Learning

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