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Accepted (AM)Open Access (License Unspecified), Open
Conference proceeding
Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring
2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), v 2020, pp 472-480
Oct 2020
PMID: 34012721
Abstract
Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size.
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Details
- Title
- Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring
- Creators
- Stephen Hansen - Drexel UniversityDaniel Schwartz - Drexel UniversityJesse Stover - Drexel UniversityMd Abu Saleh Tajin - Drexel University,Department of Electrical and Computer Engineering,Philadelphia,PA,USAWilliam M Mongan - Drexel UniversityKapil R Dandekar - Drexel UniversityIEEE
- Publication Details
- 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), v 2020, pp 472-480
- Publisher
- IEEE
- Grant note
- National Institutes of Health (10.13039/100000002) National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Computer Science
- Web of Science ID
- WOS:000659298300074
- Scopus ID
- 2-s2.0-85099544235
- Other Identifier
- 991019167665404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Biomedical
- Mathematical & Computational Biology
- Medical Informatics