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

You might also read

Related Articles

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

Sort by
Same author

Validation of a pan-ELastography Machine-learning (ELM) score to predict clinically significant portal hypertension in compensated advanced chronic liver disease.

Journal of hepatology·2026
Same author

Author Response: Using Provenance Dispersion to Triage Uncertain Neurosymbolic OCT Diagnoses.

Translational vision science & technology·2026
Same author

Crossed cerebellar diaschisis on CT perfusion in large vessel occlusion stroke: early predictors and clinical relevance in the hyperacute phase.

Journal of neurology·2026
Same author

Cerebral Flow Dysregulation Encephalopathy Following Carotid Stenting Procedure: Multiparametric CT and EEG Characteristics.

The neurologist·2026
Same author

PHENO-RAG: An artificial intelligence tool for guideline-informed management decisions in hepatocellular carcinoma.

JHEP reports : innovation in hepatology·2026
Same author

Strengthening recovery, enduring sleep. An ecologically valid assessment of sleep quantity and quality in hybrid athletes: does training mode matter?

European journal of applied physiology·2026

Related Experiment Video

Updated: Jan 9, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

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

Published on: July 27, 2018

8.6K

Analyzing Habitual Patterns and Behavioral Discrepancies in Ambient Assisted Living: An LSTM-Based Predictive Model.

Aleksandar Miladinovic, Alessandro Biscontin, Simone Kresevic

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Intelligent monitoring systems using Passive Infrared (PIR) motion sensors and Long Short-Term Memory (LSTM) networks can accurately predict elderly occupancy patterns. This technology helps detect routine changes or emergencies, enhancing safety and independent living for seniors.

    More Related Videos

    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
    06:49

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

    Published on: December 11, 2015

    9.3K
    Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
    06:46

    Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

    Published on: August 4, 2018

    12.7K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
    11:21

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

    Published on: July 27, 2018

    8.6K
    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
    06:49

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

    Published on: December 11, 2015

    9.3K
    Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
    06:46

    Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

    Published on: August 4, 2018

    12.7K

    Area of Science:

    • Gerontology and Health Informatics
    • Artificial Intelligence in Healthcare
    • Sensor Technology and Data Analytics

    Background:

    • The aging global population necessitates advanced solutions for elderly safety and independent living.
    • Ambient Assisted Living (AAL) technologies, including Passive Infrared (PIR) motion sensors, are crucial for monitoring daily activities and detecting potential health issues.
    • Existing PIR sensor systems have limitations that can be overcome to improve occupancy prediction accuracy.

    Purpose of the Study:

    • To develop an unobtrusive in-home monitoring framework utilizing PIR sensors and Long Short-Term Memory (LSTM) networks.
    • To enhance the accuracy of occupancy prediction by analyzing habitual activity patterns in elderly individuals.
    • To identify deviations from normal routines that may signal health concerns or emergencies.

    Main Methods:

    • Deployment of strategically placed PIR sensors in a single-resident apartment.
    • Application of Long Short-Term Memory (LSTM) networks for predictive modeling of elderly occupancy behavior.
    • Forecasting occupancy over time segments of 15, 30, 45, and 60 minutes.
    • Development and utilization of a habitual disaccordance metric to quantify pattern deviations.

    Main Results:

    • The LSTM-based predictive model achieved 93.0% training accuracy and 91.4% validation accuracy on 15-minute segments.
    • The model demonstrated strong predictive performance for short-term occupancy forecasting.
    • The habitual disaccordance metric effectively identified changes in daily routines and potential emergency situations.

    Conclusions:

    • Motion-sensor systems integrated with LSTM models provide a valuable tool for analyzing habitual patterns and behavioral discrepancies in elderly individuals.
    • The framework can effectively detect routine changes or emergencies, thereby improving elderly care and safety.
    • This technology supports independent living for seniors while ensuring timely intervention and reducing the risk of adverse health outcomes.