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
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Parkinsonism: onset, progression, and mortality
Hoehn, M. M., & Yahr, M. D. (1967) · Neurology · doi:10.1212/wnl.17.5.427 · Cited by 12,107
Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez‐Martin, P., Poewe, W., Sampaio, C., Stern, M. B., Dodel, R., et al. (2008) · Movement Disorders · doi:10.1002/mds.22340 · Cited by 7,536
Epidemiology of Parkinson's disease
de Lau, L. M., & Breteler, M. M. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70471-9 · Cited by 4,110
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
Parkinsonism: Onset, progression, and mortality
Hoehn, M., & Yahr, M. (2011) · Neurology · doi:10.1212/01.wnl.0000405146.06300.91 · Cited by 1,888
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668
Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,259
Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., & Bonato, P. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2033471 · Cited by 623
Quantitative wearable sensors for objective assessment of Parkinson's disease
Maetzler, W., Domingos, J., Srulijes, K., Ferreira, J. J., & Bloem, B. R. (2013) · Movement Disorders · doi:10.1002/mds.25628 · Cited by 374
Feature Learning for Activity Recognition in Ubiquitous Computing
Plötz, T., Hammerla, N., & Olivier, P. (2011) · Newcastle University · doi:10.25405/ncl.34175319 · Cited by 337
Self-citationAutomated Assessment of Bradykinesia and Dyskinesia in Parkinson's Disease
Griffiths, R. I., Kotschet, K. E., Arfon, S., Xu, Z. M., Johnson, W. G., Drago, J., Evans, A. H., Kempster, P. A., Raghav, S., & Horne, M. K. (2012) · Journal of Parkinson s Disease · doi:10.3233/jpd-2012-11071 · Cited by 278
Epidemiology of Parkinson’s Disease
Chade, A., Kasten, M., & Tanner, C. M. (2007) · CRC Press eBooks · doi:10.3109/9781420019223-6 · Cited by 264
Clinically deployable Kinesia™ technology for automated tremor assessment
Giuffrida, J. P., Riley, D. E., Maddux, B. N., & Heldman, D. A. (2009) · Movement Disorders · doi:10.1002/mds.22445 · Cited by 251
Exploring semi-supervised and active learning for activity recognition
Stikic, M., Laerhoven, K. V., & Schiele, B. (2008) · International Semantic Web Conference · doi:10.1109/iswc.2008.4911590 · Cited by 208
Hammerla, N. Y., Kirkham, R., Andras, P., & Ploetz, T. (2013) · Proceedings of the 2013 International Symposium on Wearable Computers · doi:10.1145/2493988.2494353 · Cited by 138 · cited 2×
“We capture the statistical characteristics of the movement within a frame using the ECDF representation introduced in (Plötz, Hammerla, and Olivier 2011; Hammerla et al. 2013), which corresponds to concatenated quantile functions along with their mean.”
Self-citationClimbAX: skill assessment for climbing enthusiasts
Ladha, C., Hammerla, N. Y., Olivier, P., & Plötz, T. (2013) · Ubiquitous Computing · doi:10.1145/2493432.2493492 · Cited by 134
Self-citationAutomatic assessment of problem behavior in individuals with developmental disabilities
Plötz, T., Hammerla, N., Rozga, A., Reavis, A. R., Call, N. A., & Abowd, G. D. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370276 · Cited by 127
Self-citationUsing unlabeled data in a sparse-coding framework for human activity recognition
Bhattacharya, S., Nurmi, P., Hammerla, N., & Plötz, T. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2014.05.006 · Cited by 116
Self-citationEpidemiology of Parkinson's disease
Liu, S., & Chan, P. (2016) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.3969/cjcnn.v16i2.1351 · Cited by 115
Feasibility of home-based automated Parkinson's disease motor assessment
Mera, T. O., Heldman, D. A., Espay, A. J., Payne, M., & Giuffrida, J. P. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.09.019 · Cited by 111
Use and interpretation of on/off diaries in Parkinson's disease
Reimer, J. (2004) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2003.022780 · Cited by 85
Palmer, J. L., Coats, M. A., Roe, C. M., Hanko, S. M., Xiong, C., & Morris, J. C. (2010) · Journal of Advanced Nursing · doi:10.1111/j.1365-2648.2010.05313.x · Cited by 78
Hoff, J. I., van der Meer, V., & van Hilten, J. J. (2004) · Clinical Neuropharmacology · doi:10.1097/00002826-200403000-00002 · Cited by 64
Automated Levodopa-induced dyskinesia assessment
Tsipouras, M. G., Tzallas, A. T., Rigas, G., Bougia, P., Fotiadis, D. I., & Konitsiotis, S. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626130 · Cited by 44
Cueing for drooling in Parkinson's disease
McNaney, R., Lindsay, S., Ladha, K., Ladha, C., Schofield, G. P., Plötz, T., Hammerla, N., Jackson, D., Walker, R. W., Miller, N., et al. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979030 · Cited by 29
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Research works printed but not linked: 1
- McNaney, R.; Lindsay, S.; Ladha, K.; Ladha, C.; Schofield, G.; Pl¨ otz, T.; Hammerla, N.; Jackson, D.; Walker, R.; Miller, N.; and Olivier, P. 2011. Cueing Swallowing in Parkinson's Disease. In Proc. ACM CHI Conference on Human Factors in Computing Systems .
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Sorted by citations
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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