PD disease state assessment in naturalistic environments using deep learning
- Submitting institution
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University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 359
- Type
- E - Conference contribution
- DOI
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-
- Title of conference / published proceedings
- AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
- First page
- 1742
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- January
- Year of publication
- 2015
- URL
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https://dl.acm.org/doi/10.5555/2886521.2886562
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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5
- Research group(s)
-
-
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This pioneering application of deep learning to human activity recognition is also one of the first analyses of data from wearable high-precision and high-frequency tri-axial accelerometers in real life and laboratory use. The approach led to many follow up studies (e.g. Karlen, ETH Zurich - https://doi.org/fgds; Plötz, Georgia Tech - https://doi.org/gfkpn5; Hirche, TU Munich - https://doi.org/fgdt). The work has high impact on the design and implementation of machine learning applications with wearable devices aimed to improve the quality of life of Parkinson's Disease patients.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -