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

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

213
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
213

You might also read

Related Articles

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

Sort by
Same author

Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.

Scientific reports·2026
Same author

Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires.

Sensors (Basel, Switzerland)·2025
Same author

Towards Developing a Robust Intrusion Detection Model Using Hadoop-Spark and Data Augmentation for IoT Networks.

Sensors (Basel, Switzerland)·2022
Same author

The impact of semantics on aspect level opinion mining.

PeerJ. Computer science·2021
Same author

Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging.

Sensors (Basel, Switzerland)·2021
Same author

Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks.

Sensors (Basel, Switzerland)·2020
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 15, 2026

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

Personalized Smart Home Automation Using Machine Learning: Predicting User Activities.

Mark M Gad1, Walaa Gad2, Tamer Abdelkader3

  • 1Media Engineering and Technology (MET) Department, German University in Cairo, Cairo 11835, Egypt.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a smart home automation framework using machine learning to predict user activities for enhanced comfort and energy efficiency. The Edge Light Human Activity Recognition Predictor (EL-HARP) system uses affordable hardware and gradient-boosting models for personalized, real-time control.

Keywords:
context-aware systemsedge computinggradient boosting modelshuman activity recognitionintelligent environmentsmachine learningpersonalizationsmart home automation

More Related Videos

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

5.3K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Related Experiment Videos

Last Updated: Jan 15, 2026

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
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

5.3K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

Area of Science:

  • Artificial Intelligence
  • Smart Home Technology
  • Internet of Things (IoT)

Background:

  • Smart home automation aims to enhance occupant comfort and optimize energy consumption through context-aware control.
  • Accurate prediction of user activities is crucial for proactive and personalized smart home functionalities.

Purpose of the Study:

  • To introduce a personalized framework for smart home automation leveraging machine learning for user activity prediction.
  • To develop and evaluate the Edge Light Human Activity Recognition Predictor (EL-HARP) model for real-time behavior prediction.

Main Methods:

  • Utilized affordable hardware (Raspberry Pi 5, ESP32-CAM) and open-source software for sensing and control.
  • Trained three gradient-boosting models (XGBoost, CatBoost, LightGBM) using engineered features and historical data.
  • Optimized the framework for edge deployment, focusing on efficient training and handling class imbalance.

Main Results:

  • LightGBM demonstrated strong predictive performance within the EL-HARP model, especially with extended temporal features.
  • The framework achieved real-time performance and adaptability to individual user behavior patterns.
  • A functional prototype validated the system's capabilities for context-aware smart home control.

Conclusions:

  • The developed framework offers a scalable, privacy-preserving, and user-centric approach to intelligent home automation.
  • This research advances the potential for personalized and proactive smart living environments.
  • The EL-HARP model provides a robust solution for activity recognition in edge-based smart home systems.