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 Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain Tumor Detection: Algorithm Development and Validation.

JMIR medical informatics·2026

Same author

O-GlcNAcylation expression predicts a favorable prognosis and mitigates malignant phenotypes via MYCN suppression in neuroblastoma.

Molecular and cellular pediatrics·2026

Same author

Objective tremor monitoring using tri-axis accelerometer in MRgFUS thalamotomy for essential tremor: a feasibility study.

Therapeutic advances in neurological disorders·2026

Same author

Intraoperative Suction-Assisted Kyphoplasty for Immediate Removal of Posterior PMMA Extravasation in Decompressed Vertebral Compression Fractures.

Journal of clinical medicine·2026

Same author

Home Sleep Endoscopy: Evaluating the Feasiblity of a Remote Diagnostic Tool.

The Laryngoscope·2026

Same author

Machine Learning-Based Toothbrushing Region Recognition Using Smart Toothbrush Holder and Wearable Sensors.

Biosensors·2025

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

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.

Chia-Yeh Hsieh¹, Kai-Chun Liu², Chih-Ning Huang³

¹Department of Biomedical Engineering, National Yang-Ming University, Taipei 112, Taiwan. kerrhsieh@ym.edu.tw.

Sensors (Basel, Switzerland)

|February 18, 2017

Summary

This summary is machine-generated.

This study introduces a novel hierarchical fall detection algorithm for seniors. The system reliably detects falls, improving safety and emergency response in home healthcare settings.

Keywords:

fall detection algorithm multiphase fall model wearable sensor

More Related Videos

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Published on: March 19, 2020

Related Experiment Videos

Last Updated: Mar 7, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Published on: March 19, 2020

Area of Science:

Gerontology and Biomedical Engineering
Focuses on improving elderly safety through technological solutions.

Background:

Falls are a major cause of injury and death in the elderly population.
Existing fall prevention strategies are insufficient, necessitating reliable fall detection systems.
Automatic fall detection offers real-time emergency alerts, enhancing home healthcare safety.

Purpose of the Study:

To develop a novel hierarchical fall detection algorithm addressing variability and ambiguity.
To improve the reliability, adaptability, and flexibility of automatic fall detection systems.

Main Methods:

Proposed a hierarchical fall detection algorithm combining threshold-based and knowledge-based approaches.
Utilized a multiphase fall model (free fall, impact, rest) for knowledge-based detection.
Experimentally validated the algorithm using seven fall types and seven daily activities.

Main Results:

The knowledge-based algorithm achieved high performance: 99.79% sensitivity, 98.74% specificity, 99.05% precision, and 99.33% accuracy.
The hierarchical algorithm effectively managed variability and ambiguity in fall detection.
Demonstrated reliability, adaptability, and flexibility for individual differences.

Conclusions:

The proposed hierarchical fall detection algorithm successfully addresses key technical challenges in elderly fall detection.
This system enhances the safety and quality of home healthcare for the elderly.
The algorithm meets the requirements for a reliable, adaptable, and flexible automatic fall detection system.