A wearable computing platform for developing cloud-based machine learning models for health monitoring applications

Shyamal Patel, Ryan S. McGinnis, Ikaro Silva, Steve Dicristofaro, Nikhil Mahadevan, Elise Jortberg, Jaime Franco, Albert Martin, Joseph Lust, Milan Raj, Bryan McGrane, Paolo Depetrillo, A. J. Aranyosi, Melissa Ceruolo, Jesus Pindado, Roozbeh Ghaffari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5997-6001
Number of pages5
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2016-October
ISSN (Print)1557-170X

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Patel, S., McGinnis, R. S., Silva, I., Dicristofaro, S., Mahadevan, N., Jortberg, E., Franco, J., Martin, A., Lust, J., Raj, M., McGrane, B., Depetrillo, P., Aranyosi, A. J., Ceruolo, M., Pindado, J., & Ghaffari, R. (2016). A wearable computing platform for developing cloud-based machine learning models for health monitoring applications. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 5997-6001). [7592095] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7592095