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 Experiment Video

Updated: Jun 5, 2026

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

A mechatronic and artificial intelligence-driven framework for automated non-invasive knee abnormality screening

Vidyapati Kumar1, Muhamed Shijas1, Hrishikesh M V1

  • 1Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India.

Computer Methods in Biomechanics and Biomedical Engineering
|June 3, 2026
PubMed
Summary

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

ProsthetiX-AI: An LLM-based clinical decision support system for evidence-based prosthetic recommendations.

Health information science and systems·2026
Same author

Investigation of Functionally Graded Adherents on Failure of Socket Joint of FRP Composite Tubes.

Materials (Basel, Switzerland)·2021
Same author

Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based Hybrid Techniques.

Materials (Basel, Switzerland)·2021
Same author

A comparative assessment of two designs of hip stem using rule-based simulation of combined osseointegration and remodelling.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2019

This study introduces a low-cost, automated screening tool for knee abnormalities using surface electromyography (sEMG) and goniometers. It achieves high accuracy, offering a more accessible alternative to MRI for detecting movement impairments.

Area of Science:

  • Biomedical Engineering
  • Machine Learning in Healthcare
  • Biomechanics

Background:

  • Current knee abnormality detection methods like MRI are expensive and inaccessible.
  • Subjective clinical evaluations lack objectivity and consistency.
  • There is a need for cost-effective, automated diagnostic tools for knee conditions.

Purpose of the Study:

  • To develop an integrated mechatronic and machine learning framework for knee abnormality detection.
  • To utilize surface electromyography (sEMG) and goniometers for multimodal mobility data acquisition.
  • To introduce novel time-frequency features for improved classification accuracy.

Main Methods:

  • Collected multimodal mobility data using sEMG sensors and goniometers.
  • Engineered novel time-frequency features: Enhanced Mean Absolute Value (EMAV) and Enhanced Wavelength (EWL).
Keywords:
Artificial intelligenceensemble learningknee abnormality detectionnon-invasive screeningsEMG analysis

Related Experiment Videos

Last Updated: Jun 5, 2026

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

  • Employed the Extra Trees classifier, optimizing with novel features and validated using Friedman and Nemenyi tests. Incorporated SHAP for interpretability.

    • Main Results:

    • The Extra Trees classifier achieved a cross-validated accuracy of 94.7%.
    • Novel features (EMAV, EWL) enhanced classifier performance by 3.16% compared to conventional methods (MAV, WL).
    • Achieved 95% precision and recall, demonstrating robust diagnostic capability.

    Conclusions:

    • The developed framework offers a low-cost, automated solution for knee abnormality screening.
    • This approach enhances accessibility and objectivity in diagnosing knee conditions.
    • The framework is extendable to broader human movement analysis and pathology detection applications.