The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.
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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“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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Cites
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Research works printed but not linked: 1
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Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
“For multi-modal signals, it is better to use CNN since (Bhattacharya and Lane, 2016) Body-worn, ambiemt RBM Gesture, ADL, transportation Self, D01 (Chen and Xue, 2015) Body-worn CNN ADL Self (Chen et al., 2016b) Body-worn CNN ADL D06 (Edel and K¨oppe, 2016) Body-worn RNN ADL D01, D04, Self (Fang and Hu, 2014) Object,…”
BackgroundMethodInfluentialNweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930
Using recurrent neural network models for early detection of heart failure onset
Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw112 · Cited by 928
Learning to Diagnose with LSTM Recurrent Neural Networks
Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015) · arXiv · doi:10.48550/arxiv.1511.03677 · Cited by 879
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“In [118], DBNs are employed in Parkinson’s disease diagnosis to explore if they can cope with the unreliable labelling that results from naturalistic recording environments.”
Background
Metadata
- 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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