Please use this identifier to cite or link to this item:
/library/oar/handle/123456789/131783| Title: | Time series features from foot temperature data to discriminate between diabetes-affected and healthy feet |
| Authors: | Borg, Mark Mizzi, Stephen Mifsud, Tiziana Modestini, Chiara Mizzi, Anabelle Bajada, Josef Falzon, Owen |
| Keywords: | Foot -- Thermographic methods Diabetic foot -- Diagnosis Time-series analysis Machine learning |
| Issue Date: | 2023-12 |
| Publisher: | Institute of Electrical and Electronics Engineers |
| Citation: | Borg, M., Mizzi, S., Mifsud, T., Modestini, C., Mizzi, A., Bajada, J., & Falzon, O. (2023, December). Time series features from foot temperature data to discriminate between diabetes-affected and healthy feet. Proceedings of the 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology. Malta. 29-30. |
| Abstract: | In this work, we describe the use of time series features extracted from foot temperature data obtained from a wearable in-shoe system to discriminate between feet from individuals affected by diabetes, and feet from healthy individuals. We identify a set of features that are statistically significant in discriminating between the two classes and that thus can serve as input to machine learning classifiers. |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/131783 |
| Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Time series features from foot temperature data to discriminate between diabetes affected and healthy feet 2023.pdf Restricted Access | 755.7 kB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.