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

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

Preparing the next generation of nurse leaders and innovators: Betty Irene Moore Fellowship overview and special issue introduction.

Nursing outlook·2026
Same author

Cultivating an optimal environment for nursing innovation.

Nursing outlook·2026
Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026
Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026
Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026
Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Dec 13, 2025

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

8.5K

Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection.

Gina Sprint, Diane J Cook, Roschelle Fritz

    IEEE Journal of Biomedical and Health Informatics
    |August 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Smart home sensor data reveals behavioral changes in individuals with cognitive impairment. Our BCD-G algorithm detects differences in daily routines and walking speed, aiding early health concern identification.

    More Related Videos

    A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
    08:38

    A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

    Published on: November 21, 2019

    8.0K
    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

    9.2K

    Related Experiment Videos

    Last Updated: Dec 13, 2025

    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

    8.5K
    A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
    08:38

    A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

    Published on: November 21, 2019

    8.0K
    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

    9.2K

    Area of Science:

    • Ubiquitous computing and the Internet of Things (IoT).
    • Human-computer interaction and behavioral analytics.
    • Gerontology and digital health.

    Background:

    • Smart home sensor data offers unobtrusive, longitudinal insights into resident behavior.
    • Analyzing routine behavior changes can indicate health concerns or lifestyle shifts.
    • Existing methods may not effectively compare behavioral patterns across different groups.

    Purpose of the Study:

    • To propose and evaluate a novel algorithmic framework, Behavior Change Detection for Groups (BCD-G), for analyzing group-level behavioral differences from smart home data.
    • To test the hypothesis that BCD-G can quantify and characterize behavioral disparities between groups of smart home residents.
    • To explore the utility of BCD-G in identifying health-related behavioral markers.

    Main Methods:

    • Development of the Behavior Change Detection for Groups (BCD-G) algorithmic framework based on change point detection.
    • Collection of one month of continuous sensor data from fourteen smart home residents.
    • Division of participants into two groups: those with cognitive impairment and age-matched healthy controls.

    Main Results:

    • The BCD-G framework successfully identified significant behavioral differences between the cognitively impaired and control groups.
    • Detected alterations in the patterns of activities of daily living among cognitively impaired individuals.
    • Quantified differences in clinically relevant features, such as in-home walking speed, for the impaired group.

    Conclusions:

    • The BCD-G framework provides a viable method for detecting and characterizing group-level behavioral changes using smart home sensor data.
    • Smart home monitoring can reveal subtle behavioral shifts indicative of cognitive impairment.
    • Clinicians can leverage BCD-G for remote, early identification of health concerns through unobtrusive behavioral monitoring.