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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

310
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
310
Behaviorism01:28

Behaviorism

6.8K
The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
6.8K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.2K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Factors influencing older adults' acceptance and usability of assistive technology services: A longitudinal multilevel analysis.

International journal of medical informatics·2026
Same author

Understanding knowledge and skills requirements for healthcare professionals to enable safe and effective use of physically assistive robotics.

Disability and rehabilitation. Assistive technology·2026
Same author

Pressure sensing technology for remote control: Can we motivate users to stay on the learning curve?

PloS one·2026
Same author

Public acceptance of cybernetic avatars in the service sector: evidence from a large-scale survey.

Frontiers in robotics and AI·2026
Same author

Assistive Robotics for Healthy Aging: A Foundational Phenomenological Co-Design Exercise.

Journal of medical Internet research·2026
Same author

Leveraging human-robot interaction and virtual reality for digital biomarkers in diagnostics and rehabilitation: a review from the Age-It Research Program.

The journals of gerontology. Series B, Psychological sciences and social sciences·2025

Related Experiment Video

Updated: Mar 3, 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

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data.

Laura Fiorini1, Filippo Cavallo2, Paolo Dario3

  • 1The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa 56025, Italy. laura.fiorini@santannapisa.it.

Sensors (Basel, Switzerland)
|May 5, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost smart home sensing method for activity recognition. Minimal sensors and unannotated data accurately cluster user behaviors, enabling personalized care.

Keywords:
behavioural modelscognitive health assessmentreal-home settingsunsupervised machine learning

More Related Videos

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.5K

Related Experiment Videos

Last Updated: Mar 3, 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
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.5K

Area of Science:

  • Gerontology
  • Computer Science
  • Ubiquitous Computing

Background:

  • Large-scale smart home sensing is hindered by high installation/maintenance costs and complex data annotation for activity classification.
  • Personalized care solutions require accurate user behavior analysis, which is challenging with current methods.

Purpose of the Study:

  • To propose a scalable, cost-effective method for analyzing user behavior patterns using minimal sensors and unannotated data.
  • To develop a "blind" approach for activity recognition and individual behavior clustering.

Main Methods:

  • Collected and analyzed 55 days of sensor data from 17 older adults in community-based housing.
  • Utilized a minimal sensor configuration (three sensors) and extracted features, including a "busyness" measure.
  • Employed unannotated data analysis and a "blind" approach for activity recognition and behavior clustering.

Main Results:

  • Developed robust models capable of clustering individuals based on behavior patterns with >85% accuracy.
  • Demonstrated that a minimal sensor setup and specific features are sufficient for accurate behavioral analysis.
  • Successfully described individual behavior patterns across different times of the day.

Conclusions:

  • The proposed method offers a scalable solution for optimizing personalized care through low-cost sensing and analysis.
  • This approach enables continuous tracking of individual needs, facilitating ongoing fine-tuning of care plans.
  • Highlights the potential of minimal sensor networks for effective smart home health monitoring.