PD Disease State Assessment in Naturalistic Environments Using Deep Learning
Open accessAbstract
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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Several works have introduced systems and methods for monitoring and assessing Parkinson disease symptoms.
“Specifically, several works have introduced systems and methods for monitoring and assessing Parkinson disease (PD) symptoms [31,32,33,34,35,36].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [31]
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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)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2015-02-18
- Volume
- 29
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- 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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