Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Measurement of Air Content in Concrete01:23

Measurement of Air Content in Concrete

Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
The pressure method,...
Factors Affecting Pulmonary Ventilation01:19

Factors Affecting Pulmonary Ventilation

Besides the pressure difference between the external environment and the lungs, the airflow rate and ease of pulmonary ventilation are also influenced by three other factors: surface tension of the fluid in the alveoli, compliance of the lungs, and airway resistance.
Alveolar Surface Tension
The alveolar fluid lines the luminal surface of the alveoli and exerts a force called surface tension. This force is caused by the polar water molecules in the liquid being more strongly attracted to each...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ecological momentary assessment suggests greater sensitivity to clinical change in a compensatory strategy pilot clinical trial.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026
Same author

Temperature discomfort impairs everyday cognition: a pilot study using smartwatch-based ecological momentary assessment.

Environmental research communications·2026
Same author

Promoting digital memory aid use in older adults with cognitive concerns: A pilot randomized controlled trial of adaptive web-based training.

Neuropsychology·2026
Same author

Introductory editorial for a special issue on artificial intelligence in neuropsychology.

The Clinical neuropsychologist·2026
Same author

Smart Home Technologies for Monitoring Cancer Symptoms and Enhancing Palliative Care.

Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science·2026
Same author

Detecting the Impact of Older Adult Healthy Brain Aging Behaviour Adoption Using Smart Home Technology.

Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science·2026
Same journal

Using continuous sensor data to formalize a model of in-home activity patterns.

Journal of ambient intelligence and smart environments·2025
Same journal

Clustering Home Activity Distributions for Automatic Detection of Mild Cognitive Impairment in Older Adults.

Journal of ambient intelligence and smart environments·2016
Same journal

Learning a Taxonomy of Predefined and Discovered Activity Patterns.

Journal of ambient intelligence and smart environments·2014
Same journal

Sensory grammars for sensor networks.

Journal of ambient intelligence and smart environments·2011
Same journal

On the Disambiguation of Passively Measured In-home Gait Velocities from Multi-person Smart Homes.

Journal of ambient intelligence and smart environments·2011
Same journal

Ambient Intelligence and Wearable Computing: Sensors on the Body, in the Home, and Beyond.

Journal of ambient intelligence and smart environments·2009
See all related articles

Related Experiment Video

Updated: Jun 1, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

Predicting Air Quality in Smart Environments.

Seun Deleawe1, Jim Kusznir, Brian Lamb

  • 1Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA.

Journal of Ambient Intelligence and Smart Environments
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study uses machine learning to predict carbon dioxide (CO2) levels, an air quality indicator, in smart environments. Our methods analyze sensor data and resident activities to ensure healthier living and working spaces.

More Related Videos

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry
08:23

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry

Published on: June 14, 2018

Related Experiment Videos

Last Updated: Jun 1, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry
08:23

Analyzing the Photo-oxidation of 2-propanol at Indoor Air Level Concentrations Using Field Asymmetric Ion Mobility Spectrometry

Published on: June 14, 2018

Area of Science:

  • Environmental Science
  • Computer Science
  • Health Informatics

Background:

  • Smart environments utilize pervasive sensing technologies for monitoring and assistance.
  • Indoor air quality is a critical, yet often overlooked, aspect of a healthy lifestyle.
  • Maintaining optimal air quality is essential for well-being in occupied spaces.

Purpose of the Study:

  • To investigate the application of machine learning for predicting carbon dioxide (CO2) levels as an air quality indicator.
  • To develop and analyze techniques for collecting and processing sensor data in smart environments.
  • To explore the correlation between human activities and indoor air quality dynamics.

Main Methods:

  • Implementation of machine learning algorithms for time-series forecasting of CO2 concentrations.
  • Development of data acquisition protocols for sensor networks in smart environments.
  • Statistical analysis to determine relationships between occupant behavior patterns and environmental parameters.

Main Results:

  • Machine learning models demonstrated high accuracy in predicting CO2 levels.
  • Significant correlations were identified between specific resident activities and fluctuations in air quality.
  • Validated the effectiveness of the proposed techniques across three distinct smart environment testbeds.

Conclusions:

  • Machine learning offers a viable approach for real-time air quality monitoring in smart environments.
  • Understanding activity-air quality correlations can inform strategies for maintaining healthier indoor conditions.
  • The developed methods provide a scalable solution for smart building management and occupant health.