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

Self-Initialized Locomotion Mode Prediction with GPU-Free Terrain Reconstruction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Biomechanical analysis of the flutter kick in diving: kinematics, muscle activation and coordination of lower limbs.

Journal of biomechanics·2026
Same author

High-Selectivity Proton Exchange Membranes with Low Ion Exchange Capacity and Hydrophobic Side Chain-Induced Micro-Phase Separation for Vanadium Redox Flow Batteries.

Membranes·2026
Same author

Onshore human swimming motion measurement and dynamic analysis using wearable inertial sensors.

Frontiers in bioengineering and biotechnology·2026
Same author

Ultralow CNT-reinforced phase-change fibers for scalable wearable thermoregulation.

Nature communications·2026
Same author

Personalized musculoskeletal model based multi-muscle force analysis for amputees with transtibial prostheses.

Journal of biomechanics·2025

Related Experiment Video

Updated: Aug 4, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.4K

Real-Time Wrist Motion Decoding With High Framerate Electrical Impedance Tomography (EIT).

Xiaodong Liu, Enhao Zheng, Qining Wang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 4, 2023
    PubMed
    Summary

    This study introduces a high-framerate Electrical Impedance Tomography (EIT) system for decoding human wrist motion. The EIT interface offers robust performance even after sensor re-wearing, enabling precise control for prosthetics and exoskeletons.

    More Related Videos

    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
    10:03

    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

    Published on: July 22, 2022

    4.5K
    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    677

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.4K
    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
    10:03

    Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

    Published on: July 22, 2022

    4.5K
    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    677

    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Human-Computer Interaction

    Background:

    • Decoding human motion is crucial for advanced prosthetics and exoskeletons.
    • Sensor re-wearing and external disturbances challenge existing biological-signal interfaces.
    • Myoelectric techniques, while common, lack deep muscular spatial information.

    Purpose of the Study:

    • To develop a high-framerate Electrical Impedance Tomography (EIT) system for real-time wrist motion decoding.
    • To address the challenge of sensor re-wearing and ensure robust performance.
    • To create an adaptive recognition algorithm for accurate and fast motion intent recognition.

    Main Methods:

    • Developed a high-framerate EIT system with a parallel stimulation-measurement sequence, achieving a 104 Hz sampling rate.
    • Utilized EIT to capture deep muscular spatial information, ensuring consistent muscle morphology indication.
    • Implemented an adaptive algorithm with automatic classifier mean value updates for self-calibration.
    • Validated the system on 12 subjects using a 2D Fitts' law test for wrist gesture and joint angle mapping.

    Main Results:

    • The EIT-based interface provided consistent performance even after sensor re-donning.
    • The adaptive algorithm achieved high recognition precision and fast response times.
    • Average throughputs in Fitts' law tests were comparable to surface electromyography (sEMG) based studies.
    • Demonstrated the potential of EIT for real-time human motion intent recognition.

    Conclusions:

    • The high-framerate EIT system combined with an adaptive algorithm shows significant promise for real-time wrist motion decoding.
    • The EIT interface overcomes limitations of sensor re-wearing, offering a robust alternative to myoelectric control.
    • This technology has potential applications in upper-limb exoskeleton and prosthesis control, with future research warranted for complex robotic tasks.