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

You might also read

Related Articles

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

Sort by
Same author

Magnetoacoustic tomography with magnetic induction for high-resolution bioimepedance imaging through vector source reconstruction under the static field of MRI magnet.

Medical physics·2014
Same author

Hollow superparamagnetic PLGA/Fe3O4 composite microspheres for lysozyme adsorption.

Nanotechnology·2014
Same author

[A bird's eye view of the algorithms and software packages for reconstructing phylogenetic trees].

Dong wu xue yan jiu = Zoological research·2014
Same author

Functional and biodegradable dendritic macromolecules with controlled architectures as nontoxic and efficient nanoscale gene vectors.

Biotechnology advances·2014
Same author

[Effects of artificial vegetation on the spatial heterogeneity of soil moisture and salt in coastal saline land of Chongming Dongtan, Shanghai].

Ying yong sheng tai xue bao = The journal of applied ecology·2014
Same author

TRIM14 is a mitochondrial adaptor that facilitates retinoic acid-inducible gene-I-like receptor-mediated innate immune response.

Proceedings of the National Academy of Sciences of the United States of America·2014

Related Experiment Video

Updated: Jul 2, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration.

Jie Tang, Bin He, Junkai Xu

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |February 26, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for generating synthetic inertial measurement unit (IMU) data to improve elderly fall detection. This approach reduces the need for costly experiments, enhancing machine learning model development.

    More Related Videos

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
    06:52

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

    Published on: May 26, 2020

    7.9K
    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
    11:14

    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

    Published on: October 4, 2015

    10.9K

    Related Experiment Videos

    Last Updated: Jul 2, 2025

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.7K
    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
    06:52

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

    Published on: May 26, 2020

    7.9K
    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
    11:14

    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

    Published on: October 4, 2015

    10.9K

    Area of Science:

    • Biomedical Engineering
    • Computer Science
    • Gerontology

    Background:

    • Falls are a major cause of injury in the elderly.
    • Wearable inertial measurement unit (IMU) sensors and machine learning are used for fall detection.
    • Acquiring sufficient training data for fall detection models is expensive and challenging.

    Purpose of the Study:

    • To develop a novel method for generating synthetic IMU data for fall detection.
    • To reduce the cost and complexity of fall detection data acquisition.
    • To optimize IMU sensor placement and configuration for improved fall detection.

    Main Methods:

    • Utilizing 3D motion capture to reconstruct human movements.
    • Employing the Opensim biomechanical simulation platform and forward kinematics to generate synthetic IMU data.
    • Training machine learning models on synthetic data and evaluating performance on real-world fall datasets.

    Main Results:

    • Achieved high testing accuracies of 91.99% and 86.62% on two distinct real-world fall datasets.
    • Demonstrated the effectiveness of synthetic data in training accurate fall detection models.
    • Optimized single IMU attachment positions and multiple IMU combinations for enhanced fall detection.

    Conclusions:

    • The proposed method significantly simplifies fall detection data acquisition.
    • It offers a cost-effective solution for generating synthetic data where real-world data is scarce.
    • This framework facilitates the customization of machine learning configurations for fall detection systems.