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

doi:10.1109/EMBC.2016.7592095

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

QSIB - Querrey Simpson Institute for Bioelectronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 language	English (US)
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5997-6001
Number of pages	5
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7592095
State	Published - Oct 13 2016
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October
ISSN (Print)	1557-170X

Other

Other	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	8/16/16 → 8/20/16

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2016.7592095

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

Patel, Shyamal ; McGinnis, Ryan S. ; Silva, Ikaro et al. / A wearable computing platform for developing cloud-based machine learning models for health monitoring applications. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 5997-6001 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "A wearable computing platform for developing cloud-based machine learning models for health monitoring applications",

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.",

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

year = "2016",

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doi = "10.1109/EMBC.2016.7592095",

language = "English (US)",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5997--6001",

booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",

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Patel, S, McGinnis, RS, Silva, I, Dicristofaro, S, Mahadevan, N, Jortberg, E, Franco, J, Martin, A, Lust, J, Raj, M, McGrane, B, Depetrillo, P, Aranyosi, AJ, 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., 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., pp. 5997-6001, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 8/16/16. https://doi.org/10.1109/EMBC.2016.7592095

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications. / Patel, Shyamal; McGinnis, Ryan S.; Silva, Ikaro et al.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 5997-6001 7592095 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Patel, Shyamal

AU - McGinnis, Ryan S.

AU - Silva, Ikaro

AU - Dicristofaro, Steve

AU - Mahadevan, Nikhil

AU - Jortberg, Elise

AU - Franco, Jaime

AU - Martin, Albert

AU - Lust, Joseph

AU - Raj, Milan

AU - McGrane, Bryan

AU - Depetrillo, Paolo

AU - Aranyosi, A. J.

AU - Ceruolo, Melissa

AU - Pindado, Jesus

AU - Ghaffari, Roozbeh

PY - 2016/10/13

Y1 - 2016/10/13

N2 - 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.

AB - 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.

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Y2 - 16 August 2016 through 20 August 2016

ER -

Patel S, McGinnis RS, Silva I, Dicristofaro S, Mahadevan N, Jortberg E et al. 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. Institute of Electrical and Electronics Engineers Inc. 2016. p. 5997-6001. 7592095. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7592095

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

Abstract

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