University of Massachusetts Medical School Faculty Publications
Title
Harmonizing Wearable Biosensor Data Streams to Test Polysubstance Detection
UMMS Affiliation
Department of Population and Quantitative Health Sciences; Department of Emergency Medicine; Department of Psychiatry
Publication Date
2020-02-01
Document Type
Conference Proceeding
Disciplines
Biomedical Devices and Instrumentation | Computer Sciences | Data Science | Health Services Administration | Medical Toxicology | Substance Abuse and Addiction
Abstract
Wearable biosensors, as a key component of wireless body area network (WBAN) systems, have extended the ability of health care providers to achieve continuous health monitoring. Prior research has shown the ability of externally placed, non-invasive sensors combined with machine learning algorithms to detect intoxication from a variety of substances. Such approaches have also shown limitations. The difficulties in developing a model capable of detecting intoxication generally include differences among human beings, sensors, drugs, and environments. This paper examines how approaching wireless communication advances and new paradigms in constructing distributed systems may facilitate polysubstance use detection. We perform supervised learning after harmonizing two types of offline data streams containing wearable biosensor readings from users who had taken different substances, accurately classifying 90% of samples. We examine time domain and frequency domain features and find that skin temperature and mean acceleration are the most important predictors.
Keywords
addiction, biosensor, classification, data stream, feature extraction
DOI of Published Version
10.1109/icnc47757.2020.9049684
Source
Rumbut J, Fang H, Wang H, Carreiro S, Smelson D, Chapman B, Boyer E. Harmonizing Wearable Biosensor Data Streams to Test Polysubstance Detection. Int Conf Comput Netw Commun. 2020 Feb;2020:445-449. doi: 10.1109/icnc47757.2020.9049684. Epub 2020 Mar 30. PMID: 33732746; PMCID: PMC7962664. Link to article on publisher's site
Related Resources
Journal/Book/Conference Title
International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications
PubMed ID
33732746
Repository Citation
Rumbut J, Fang H(, Wang H, Carreiro S, Smelson DA, Chapman B, Boyer E. (2020). Harmonizing Wearable Biosensor Data Streams to Test Polysubstance Detection. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1109/icnc47757.2020.9049684. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/1962