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

Energy and Power Signals01:17

Energy and Power Signals

716
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
716
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

214
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
214

You might also read

Related Articles

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

Sort by
Same author

Emergency Detection in Smart Homes Using Inactivity Score for Handling Uncertain Sensor Data.

Sensors (Basel, Switzerland)·2024
Same author

Potential of Assistive Robots in Clinical Nursing: An Observational Study of Nurses' Transportation Tasks in Rural Clinics of Bavaria, Germany.

Nursing reports (Pavia, Italy)·2024
Same author

Nurses' Workplace Perceptions in Southern Germany-Job Satisfaction and Self-Intended Retention towards Nursing.

Healthcare (Basel, Switzerland)·2024
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: Oct 10, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

6.0K

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a

Sebastian Wilhelm1, Jakob Kasbauer1

  • 1Deggendorf Institute of Technology, 94469 Deggendorf, Germany.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel non-intrusive load monitoring (NILM) method using pattern recognition on smart meter data to detect appliance usage. This approach enables near real-time human activity recognition (HAR) within homes.

Keywords:
ambient assisted living (AAL)ambient intelligence (AmI)human activity recognition (HAR)internet of things (IoT)motif searchnon-intrusive load monitoring (NILM)smart meter

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

9.0K
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

8.4K

Related Experiment Videos

Last Updated: Oct 10, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

6.0K
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

9.0K
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

8.4K

Area of Science:

  • * Computer Science
  • * Electrical Engineering
  • * Artificial Intelligence

Background:

  • * Non-intrusive load monitoring (NILM) typically focuses on energy disaggregation for monitoring purposes.
  • * Existing NILM methods often lack the granularity for detailed human activity recognition (HAR).
  • * Smart meter data offers a rich, albeit complex, source for inferring household activities.

Purpose of the Study:

  • * To develop a novel NILM approach for disaggregating household power consumption to detect individual appliance actions.
  • * To enable near real-time human activity recognition (HAR) by analyzing raw power waveforms.
  • * To quantify disaggregation uncertainty using continuous pattern correlation.

Main Methods:

  • * Pattern recognition applied to raw smart meter power waveforms.
  • * Edge computing for real-time, streaming data analysis.
  • * Motif-detection algorithms for appliance action identification.
  • * Continuous pattern correlation for uncertainty quantification.

Main Results:

  • * Demonstrated the feasibility of the NILM approach for appliance action detection using real household data.
  • * Achieved near real-time detection capabilities suitable for streaming applications.
  • * Showcased the potential for using disaggregated appliance data for human activity recognition.
  • * Disaggregation quality was found to be dependent on pattern selection and appliance type.

Conclusions:

  • * A novel NILM approach effectively disaggregates power consumption to identify individual appliance actions.
  • * The method enables near real-time human activity recognition by analyzing smart meter data.
  • * Quantifying uncertainty via continuous pattern correlation offers a more nuanced understanding than binary states.