Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Subset selection based fusion for biomedical information retrieval tasks.

BMC bioinformatics·2025

Same author

Combating health misinformation with fusion-based credible retrieval techniques.

Health informatics journal·2025

Same author

GSAformer: Group sparse attention transformer for functional brain network analysis.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

Miniformer: A Minimalist Transformer for Brain Functional Networks Analysis.

IEEE journal of biomedical and health informatics·2025

Same author

Oropharyngeal gonorrhoea infections among young heterosexual users of online sexual health services across the island of Ireland.

Sexually transmitted infections·2025

Same author

Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition.

IEEE journal of biomedical and health informatics·2024

Search research articles

Related Experiment Video

Updated: Apr 9, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Simulation of Smart Home Activity Datasets.

Jonathan Synnott¹, Chris Nugent², Paul Jeffers³

¹School of Computing and Mathematics, University of Ulster, Jordanstown, County Antrim BT37 0QB, UK. j.synnott@ulster.ac.uk.

Sensors (Basel, Switzerland)

|June 19, 2015

Summary

Simulating smart home environments aids in creating datasets for activity recognition, addressing challenges in real-world sensor data collection for an aging population. This research reviews simulation methods to advance intelligent environment monitoring.

Keywords:

monitoring simulation smart environments visualization

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Related Experiment Videos

Last Updated: Apr 9, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Area of Science:

Gerontology
Computer Science
Health Informatics

Background:

Global population aging increases chronic conditions, demanding long-term care and straining healthcare resources.
Intelligent environments (smart homes) use sensors for monitoring activities, crucial for managing health and quality of life in older adults.
Access to real-world sensor data for developing activity monitoring is limited by cost, availability, and deployment.

Purpose of the Study:

To review existing methods for generating simulated smart home activity datasets.
To address the limitations of real-world sensor data acquisition for research.
To facilitate the development of advanced activity monitoring and recognition approaches.

Main Methods:

Review of model-based approaches for smart home simulation.
Analysis of interactive approaches using virtual sensors, environments, and avatars.
Evaluation of simulation techniques for generating comprehensive activity datasets.

Main Results:

Identified various model-based and interactive simulation approaches for smart home environments.
Highlighted the potential of simulation to overcome data access barriers.
Provided a foundation for understanding current simulation capabilities.

Conclusions:

Simulated smart home environments offer a viable solution for generating essential activity datasets.
Further research in intelligent environment simulation is recommended to enhance activity monitoring technologies.
Simulation is key to advancing smart home applications for an aging population.