Jove
Visualize
Contact Us

Related Concept Videos

Classification of Signals01:30

Classification of Signals

773
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
773
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Biomechanics in Sport and Motion Analysis.

Bioengineering (Basel, Switzerland)·2026
Same author

Online Estimation of Manipulator Dynamics for Computed Torque Control of Robotic Systems.

Sensors (Basel, Switzerland)·2025
Same author

Gait and Postural Control Deficits in Diabetic Patients with Peripheral Neuropathy Compared to Healthy Controls.

Bioengineering (Basel, Switzerland)·2025
Same author

How does green human resource management foster employees' environmental commitment: A sequential mediation analysis.

Heliyon·2024
Same author

Editorial: From depth (needle) to surface: electromyography as a diagnostic tool in identifying neuromuscular changes associated with neurological disorders.

Frontiers in human neuroscience·2023
Same author

Craniocaudal toggling increases the risk of screw loosening in osteoporotic vertebrae.

Computer methods and programs in biomedicine·2023
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 Experiment Video

Updated: Aug 29, 2025

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.2K

Improved Classification Accuracy of Hand Movements Using Softmax Classifier and Kalman Filter.

Abdullah Y Al-Maliki, Kamran Iqbal

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances myoelectric lower arm prosthesis control by improving hand gesture classification from surface Electromyography (sEMG) data. Using a Kalman filter (KF) with a Softmax classifier significantly boosted accuracy to 99.3%.

    More Related Videos

    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

    714
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.0K

    Related Experiment Videos

    Last Updated: Aug 29, 2025

    Assessment and Communication for People with Disorders of Consciousness
    07:37

    Assessment and Communication for People with Disorders of Consciousness

    Published on: August 1, 2017

    9.2K
    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

    714
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.0K

    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Engineering
    • Signal Processing

    Background:

    • Effective control of myoelectric lower arm prostheses relies on accurate interpretation of surface Electromyography (sEMG) signals.
    • Current methods face challenges in precisely identifying intended hand movements from noisy sEMG data.
    • Advancements in machine learning and signal processing are crucial for improving prosthesis functionality.

    Purpose of the Study:

    • To enhance the accuracy of hand gesture classification from sEMG data for myoelectric lower arm prosthesis control.
    • To evaluate the efficacy of combining feature arrays, a Kalman filter (KF), and a Softmax classifier for improved classification.
    • To validate the proposed method using a standard dataset and multiple participants.

    Main Methods:

    • Utilized the BioPatRec database containing sEMG data from 17 participants performing ten distinct hand movements.
    • Implemented a classification system incorporating feature arrays and a Softmax classifier.
    • Applied a Kalman filter (KF) to pre-process and smooth the training sEMG data before classification.

    Main Results:

    • The Softmax classifier achieved an initial accuracy of 95.3% without Kalman filter pre-processing.
    • Incorporating the Kalman filter (KF) to smooth the training data resulted in a significant accuracy improvement to 99.3%.
    • The proposed method demonstrated high reliability in classifying ten different hand movements.

    Conclusions:

    • The integration of feature arrays, Kalman filtering, and a Softmax classifier offers a highly accurate method for sEMG-based hand gesture recognition.
    • Kalman filter pre-processing is a key factor in achieving near-perfect classification accuracy for myoelectric control.
    • This approach holds significant potential for advancing the performance and usability of lower arm prostheses.