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

Understanding Sleep01:11

Understanding Sleep

1.8K
Sleep, an essential biological state, involves significant reductions in physical activity, sensory awareness, and interaction with the environment. This complex physiological process is primarily regulated by specific brain regions, notably the hypothalamus and pons, which govern the sleep-wake cycle or circadian rhythm.
The circadian rhythm, a nearly 24-hour cycle, is deeply influenced by environmental light cues. Light exposure directly affects the hypothalamus, which in turn regulates...
1.8K
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

3.2K
Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
3.2K
REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

2.3K
REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...
2.3K
Stages of Sleep01:22

Stages of Sleep

1.6K
Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
1.6K
Sleepwalking and Sleep Talking01:17

Sleepwalking and Sleep Talking

1.3K
Somnambulism, commonly known as sleepwalking, involves individuals engaging in activities ranging from simple walking to more complex behaviors such as driving. Sleepwalking typically occurs during the slow-wave sleep stages 3 and 4 early in the night when the person is not dreaming, contradicting the myth that sleepwalkers are acting out their dreams.
Factors that increase the likelihood of sleepwalking include sleep deprivation and alcohol consumption. Contrary to common beliefs, it is safe...
1.3K

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

The role of digital resources in surgical education: An analysis of YouTube videos on dynamic stabilization.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
Same journal

Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
Same journal

Leveraging personal health records for early heart failure risk prediction through AI-driven modeling.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
Same journal

From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
Same journal

Leadership styles and work outcome in healthcare sector: Insights from bibliometric analysis.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
Same journal

Network analysis revealing research focus of the German Congress of Orthopedics and Trauma Surgery 2021.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 14, 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

8.9K

Forecasting behavior in smart homes based on sleep and wake patterns.

Jennifer A Williams, Diane J Cook

    Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
    |October 1, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Smart home technology helps individuals with injuries or disabilities live independently. The Behavior Forecasting (BF) algorithm accurately models and predicts wake and sleep patterns, improving daily living assistance.

    Keywords:
    Machine learningbehavior forecastingsleep analysissmart environments

    More Related Videos

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
    08:36

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

    Published on: August 8, 2019

    12.9K
    Multi-Modal Home Sleep Monitoring in Older Adults
    07:40

    Multi-Modal Home Sleep Monitoring in Older Adults

    Published on: January 26, 2019

    8.3K

    Related Experiment Videos

    Last Updated: Mar 14, 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

    8.9K
    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
    08:36

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

    Published on: August 8, 2019

    12.9K
    Multi-Modal Home Sleep Monitoring in Older Adults
    07:40

    Multi-Modal Home Sleep Monitoring in Older Adults

    Published on: January 26, 2019

    8.3K

    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Biomedical Engineering

    Background:

    • Smart home technology offers potential for independent living support for individuals with disabilities or recovering from injuries.
    • Monitoring daily behaviors is crucial for assessing well-being and providing tailored assistance.

    Purpose of the Study:

    • To introduce and evaluate the Behavior Forecasting (BF) algorithm for modeling and predicting wake and sleep behaviors.
    • To investigate the bidirectional influence between wake and sleep patterns.
    • To enhance prediction accuracy by integrating both wake and sleep data.

    Main Methods:

    • The Behavior Forecasting (BF) algorithm was developed, involving numerical representation of sleep/wake states.
    • Forecasting of future wake and sleep values based on historical data.
    • Analysis of the interdependencies between wake and sleep behaviors.
    • Optimization of prediction models using combined wake and sleep metrics.

    Main Results:

    • The BF algorithm demonstrated robust performance in modeling wake and sleep behaviors, achieving a minimum accuracy of 84% across 20 smart homes.
    • Normalizing wake and sleep scores significantly enhanced prediction accuracy to 99%.
    • The study confirmed that wake behaviors can be predicted from sleep patterns and vice versa.

    Conclusions:

    • Wake and sleep behaviors can be effectively modeled within smart home environments.
    • The BF algorithm provides a reliable method for predicting daily activity patterns.
    • This technology can enhance support systems for independent living.