Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multimachine Stability

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

You might also read

Related Articles

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

Sort by

Same author

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper.

Entropy (Basel, Switzerland)·2024

Same author

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds.

Entropy (Basel, Switzerland)·2023

Same author

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN.

Entropy (Basel, Switzerland)·2022

Same author

Ensemble Approach for Detection of Depression Using EEG Features.

Entropy (Basel, Switzerland)·2022

Same author

Action Recognition Using Single-Pixel Time-of-Flight Detection.

Entropy (Basel, Switzerland)·2020

Same author

A unified weighted minimum norm solution for the reference inverse problem in EEG.

Computers in biology and medicine·2019

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Fall detection algorithm in energy efficient multistate sensor system.

Gundars Korats, Janis Hofmanis, Aleksejs Skorodumovs

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 7, 2016

Summary

This summary is machine-generated.

This study introduces an energy-efficient sensor system using a 3-axial accelerometer for detecting falls in elderly individuals. The system achieves 100% accuracy with proper calibration, significantly extending battery life for continuous health monitoring.

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: Mar 27, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

Biomedical Engineering
Gerontology
Wearable Technology

Background:

Elderly individuals face significant injury risks from falls during daily activities.
Existing fall detection systems require improvements in sensor design, accuracy, and energy efficiency.
Timely detection of falls is crucial for mitigating severe health consequences in the elderly population.

Purpose of the Study:

To develop an energy-efficient sensor system for detecting falls in elderly individuals.
To enhance the accuracy and reduce the power consumption of fall detection technology.
To improve the reliability of health monitoring systems for the aging population.

Main Methods:

Implementation of a single 3-axial accelerometer-based sensor system.
Utilizing selective event processing triggered by a fall detection algorithm for power saving.
Simulating system performance with correctly chosen threshold parameters.

Main Results:

The proposed system demonstrates 100% accuracy in fall detection under optimal threshold settings.
Significant extension of battery life is estimated due to the energy-efficient design.
The system offers a viable solution for continuous and reliable fall monitoring.

Conclusions:

The developed 3-axial accelerometer system provides an accurate and energy-efficient solution for elderly fall detection.
This technology has the potential to enhance patient safety and reduce healthcare burdens.
Further research can optimize parameters for broader clinical application and integration into daily living aids.