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

Shih-Sung Lin,

Ping-Nan Chen

и другие.

Research Square (Research Square), Год журнала: 2022, Номер unknown

Опубликована: Июнь 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.

Язык: 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

и другие.

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 111712 - 111712

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

3

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

и другие.

The Science of The Total Environment, Год журнала: 2023, Номер 895, С. 164858 - 164858

Опубликована: Июнь 19, 2023

Язык: Английский

Процитировано

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

и другие.

Heliyon, Год журнала: 2023, Номер 9(7), С. e17282 - e17282

Опубликована: Июнь 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.

Язык: Английский

Процитировано

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, Год журнала: 2023, Номер 230, С. 120670 - 120670

Опубликована: Июнь 3, 2023

Язык: Английский

Процитировано

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

и другие.

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(32), С. 44463 - 44488

Опубликована: Июнь 28, 2024

Язык: Английский

Процитировано

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

и другие.

Thermal Science and Engineering Progress, Год журнала: 2025, Номер unknown, С. 103284 - 103284

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

и другие.

International Journal of Medical Informatics, Год журнала: 2024, Номер 185, С. 105408 - 105408

Опубликована: Март 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.

Язык: Английский

Процитировано

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

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 113191 - 113191

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Building Engineering, Год журнала: 2023, Номер 80, С. 108059 - 108059

Опубликована: Ноя. 1, 2023

Язык: Английский

Процитировано

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

и другие.

Heliyon, Год журнала: 2024, Номер 10(11), С. e31716 - e31716

Опубликована: Май 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.

Язык: Английский

Процитировано

2