This paper is in the following e-collection/theme issue:

mHealth in a Clinical Setting (582) Internet of Things (70) Novel Sensors and Data Acquisition Methods in Cardiology (191) e-Cardiology and Telemonitoring in Cardiology (150)

Published on in Vol 6, No 11 (2018): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12048, first published .
A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study

A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study

A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study

Authors of this article:

Jang Ho Lee1 Author Orcid Image ;   Yu Rang Park2 Author Orcid Image ;   Solbi Kweon2 Author Orcid Image ;   Seulgi Kim1 Author Orcid Image ;   Wonjun Ji1 Author Orcid Image ;   Chang-Min Choi1, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (5) Metrics

Journals

  1. Min Hyun C, Jun Jang T, Nam J, Kwon H, Jeon K, Lee K. Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography. Machine Learning: Science and Technology 2023;4(1):015034 View
  2. An Y, Cao K, Li F, Lu Q, Guan Y, Lu Z, Wang A, Tian Z. A new classification for emergency critically ill patients and analysis of their adverse events during intrahospital transport: A cluster analysis. Nursing in Critical Care 2024 View