A Low-Cost Physiological Monitoring Interface for Intensive Care Unit DOI Creative Commons
Ke-Feng Lin,

Shih-Sung Lin,

Ping-Nan Chen

et al.

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: June 10, 2022

Abstract Background: During the COVID-19 pandemic, which emerged in 2020, many patients were treated isolation wards because of high infectivity and long incubation period COVID-19. Therefore, monitoring systems have become critical to patient care safeguard medical professional safety. Objective: The user interface is very important surveillance system; therefore, we use web technology develop a system that can create an based on needs. When scene needs be changed, location changed at any time, effectively reducing costs time required, so achieve timely appropriate goals treatment. Methods: ZigBee was employed for intensive units (ICUs). Unlike conventional GUIs, proposed GUI enables various aspects patient, modified according A simulated ICU environment designed test effectiveness system. nodes set up positions consistent with actual clinical environments measure required switch between scenes or targets GUI. Results: low-cost construct ZigBee-simulated graphical interfaces demand this study. overcome limitations design methods interfaces. Conclusion: This used simultaneously monitor basic physiological data numerous patients, enabling nursing professionals instantly determine status provide treatments. applied remote after official adoption.

Language: Английский

AI Management Platform for Privacy-Preserving Indoor Air Quality Control: Review and future directions DOI Creative Commons
Tran Van Quang, Dat Tien Doan,

Nat Jack

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111712 - 111712

Published: Jan. 1, 2025

Language: Английский

Citations

2

Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges DOI
Xilei Dai, Wenzhe Shang, Junjie Liu

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 895, P. 164858 - 164858

Published: June 19, 2023

Language: Английский

Citations

40

Studying the impacts of test condition and nonoptimal positioning of the sensors on the accuracy of the in-situ U-value measurement DOI Creative Commons
Behnam Mobaraki, Francisco Javier Castilla Pascual, Arturo Martínez

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(7), P. e17282 - e17282

Published: June 16, 2023

The non-destructive thermal characterization of building envelopes relies significantly on various factors such as climate conditions, monitoring devices used, indoor environment, and conditioning systems. In the case both temperature-based method (TBM) heat flux meter (HFM) approaches, U-value is determined considering ideal condition steady state. However, it challenging to accurately define true buildings when affected by inherent uncertainties chosen approach inadequate instrumentation envelopes. This paper presents outcomes an experimental campaign, that aimed evaluate impact incorrectly positioned exterior sensors, precision measurements. study simultaneously employed TBM HFM approaches. To enhance accuracy results, rigorous outlier detection statistical analysis were data collected from three autonomous findings this revealed applied yielded more satisfactory results for compared HFM. regardless effectiveness relied heavily When removing individual outlier, systems characterized with higher accuracies provided U-values closer theoretical values, than less accurate ones.

Language: Английский

Citations

25

SA–EMD–LSTM: A novel hybrid method for long-term prediction of classroom PM2.5 concentration DOI
Erbiao Yuan,

Guangfei Yang

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 230, P. 120670 - 120670

Published: June 3, 2023

Language: Английский

Citations

21

Revolutionizing indoor air quality monitoring through IoT innovations: a comprehensive systematic review and bibliometric analysis DOI
Huiyi Tan, Mohd Hafiz Dzarfan Othman, Hong Yee Kek

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(32), P. 44463 - 44488

Published: June 28, 2024

Language: Английский

Citations

4

Indoor environmental monitoring based on sensor data acquisition and thermal energy cycle: Design and application of artificial intelligence DOI
Chenghan Li,

Wenyu Yan,

Z. Y. Wang

et al.

Thermal Science and Engineering Progress, Journal Year: 2025, Volume and Issue: unknown, P. 103284 - 103284

Published: Jan. 1, 2025

Language: Английский

Citations

0

Categorizing digital data collection and intervention tools in health and wellbeing living lab settings: A modified Delphi study DOI Creative Commons
Despoina Petsani, Teemu Santonen, Beatriz Merino‐Barbancho

et al.

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 185, P. 105408 - 105408

Published: March 11, 2024

Health and Wellbeing Living Labs are a valuable research infrastructure for exploring innovative solutions to tackle complex healthcare challenges promote overall wellbeing. A knowledge gap exists in categorizing understanding the types of ICT tools technical devices employed by Labs. Define comprehensive taxonomy that effectively categorizes organizes digital data collection intervention Lab studies. modified consensus-seeking Delphi study was conducted, starting with pre-study involving survey semistructured interviews (N=30) gather information on existing equipment. The follow-up three rounds panel living lab experts (R1 N=18, R2 - 3 N=15) from 10 different countries focused achieving consensus category definitions, ease reading, included subitems each category. Due controversial results 2nd round qualitative feedback, an online workshop organized clarify contradictory issues. resulting 52 subitems, which were divided into levels as follows: first level consists 'devices monitoring collection' 'technologies intervention.' At second level, 'data is further 'environmental' 'human' monitoring. latter includes following third-level categories: 'biometrics,' 'activity behavioral monitoring,' 'cognitive ability mental processes,' 'electrical biosignals physiological measures,' '(primary) vital signs,' 'body size composition.' intervention' 'assistive technology,' 'extended reality – XR (VR & AR),' 'serious games' categories. common language standardized terminology established enable effective communication labs their customers. opens roadmap studies map related based functionality, features, target populations, intended outcomes, fostering collaboration enhancing capture exploitation.

Language: Английский

Citations

3

Evaluation of Energy Performance and Indoor Environmental Quality in a Retrofitted Daycare Center Using a Living Lab Approach DOI

Haijun Yan,

Jaewoo Yoon, Doosam Song

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 113191 - 113191

Published: May 1, 2025

Language: Английский

Citations

0

A virtual sensor network for pressure distribution inside a multi-zone building based on spatial adjacency relationships and multivariate adaptive regression spline DOI

Jiajun Jing,

Dongseok Lee,

Jaewan Joe

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 80, P. 108059 - 108059

Published: Nov. 1, 2023

Language: Английский

Citations

6

An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks DOI Creative Commons
Hiram Pönce, Sebastián Gutiérrez, Juan Botero-Valencia

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(11), P. e31716 - e31716

Published: May 23, 2024

Highlights•This study proposes a machine learning model for virtual hygrothermal measurement in enclosed buildings.•We use artificial hydrocarbon networks as the core intelligent climate monitoring system.•We consider two case scenarios, i.e. museum and laboratory, experimental analysis.•Results provide 95% accuracy with 0.22% tolerance variability after sensor accommodation, both scenarios.AbstractStudies analyzing indoor thermal environments comprising temperature humidity may be insufficient when obtaining data from sensors, which susceptible to inaccurate or failed information internal external factors. Therefore, this an using supervised method buildings used predict relative failure is detected. The methodology comprises collection wireless network, building of predicting dynamics environmental variables, implementation detection model. We network their simplicity effectiveness under uncertain noisy data. experiments acquired settings: (1) laboratory office (2) storage room. first scenario has multiple workstations, staff turns on off air conditioning depending feeling comfort, generating uncontrolled environment variables interest. second controlled ensure conservation conditions pieces. Both scenarios 12 sensors that one month, providing average 58,300 values each variable. Results proposed terms identification, less than accommodation scenarios.

Language: Английский

Citations

2