Yue, P., Wang, X., Yang, Y. et al. (2 more authors) (2023) Up-sampling active learning: an activity recognition method for Parkinson's disease patients. In: Tsanas, A. and Triantafyllidis, A., (eds.) Pervasive Computing Technologies for Healthcare. 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, 12-14 Dec 2022, Thessaloniki, Greece. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 488 (1). Springer ISBN 978-3-031-34586-9
Abstract
Parkinson’s Disease (PD) is the second most common neurodegenerative disease. With the advancement of technologies of big data, wearable sensing and artificial intelligence, automatically recognizing PD patients’ Physical Activities (PAs), health status and disease progress have become possible. Nevertheless, the PA measures are still facing challenges especially in uncontrolled environments. First, it is difficult for the model to recognize the PA of new PD patients. This is because different PD patients have different symptoms, diseased locations and severity that may cause significant differences in their activities. Second, collecting PA data of new PD patients is time-consuming and laborious, which will inevitably result in only a small amount of data of new patients being available. In this paper, we propose a novel up-sampling active learning (UAL) method, which can reduce the cost of annotation without reducing the accuracy of the model. We evaluated the performance of this method on the 18 PD patient activities data set collected from the local hospital. The experimental results demonstrate that this method can converges to better accuracy using a few labeled samples, and achieve the accuracy from 44.3% to 99.0% after annotating 25% of the samples. It provides the possibility to monitor the condition of PD patients in uncontrolled environments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. This is an author-produced version of a paper subsequently published in Pervasive Computing Technologies for Healthcare. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Activity Recognition; Active Learning; Parkinson's Disease; Cross-Subject |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EPSRC/Industrial 165332 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Nov 2022 12:51 |
Last Modified: | 11 Jun 2024 00:13 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193488 |