Evidence map›Paper›PMID 40634907›Full record

ArticleBMC musculoskeletal disorders2025

The cartilage-generated bioelectric potentials induced by dynamic joint movement; an exploratory study.

Jae-Hyun Lee, Ye-Seul Jang, Won-Du Chang

Abstract read
In one paragraph

Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Jae-Hyun Lee *Department of Artificial Intelligence Convergence, Pukyong National University, Yongso-ro, 45, Nam-Gu, Busan, 48513, Republic of Korea.
Ye-Seul Jang *Department of System Integration Engineering, Faculty of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, 400-8511, Japan.
Won-Du ChangDepartment of Artificial Intelligence Convergence, Pukyong National University, Yongso-ro, 45, Nam-Gu, Busan, 48513, Republic of Korea. chang@pknu.ac.kr.

Funding

National Research Foundation of Korea RS-2024-00334159Pusan National University Hospital CMIT2024-05
6 · The paper itself

Abstract

backgroundExcessive loading can damage knee cartilage, making it essential to assess and measure joint load effectively. Despite its importance, real-time monitoring of cartilage load in clinical settings remains challenging due to significant technical constraints. Electroarthrography, a recently introduced non-invasive technique, offers a promising solution by detecting load-generated potentials in joint cartilage through surface electrodes. While previous studies have primarily focused on static load applications, such as standing weight shift task or simple isometric contraction, our study explores its potential in dynamic loading scenarios.

methodsWe analyzed data from 20 knees in 20 subjects, using eight surface electrodes placed around each knee to capture electrical signals during three activities: active knee extension in a seated position, passive range of motion exercise in a decubitus position, and restricted squats. The recorded signals were processed into potential-time graphs, decomposed according to movement states, and analyzed through a deep neural network.

resultsThe results showed that cartilage-generated potentials were significantly higher during active extension compared to passive extension (1.62 mV vs. 0.87 mV; p < 0.05), with the deep neural network achieving an average classification accuracy of 98.77%.

conclusionThese findings highlight the feasibility of measuring and classifying cartilage-generated potentials during dynamic physical activities, providing valuable insights into load-related differences. This approach establishes a solid foundation for applications in rehabilitation medicine by facilitating the determination of appropriate exercise intensities, assessing risks associated with daily activities, and classifying physical activities. Further studies focusing on diverse biomechanical conditions will enhance its clinical utility.

Indexed as

Cartilage, ArticularKnee JointRange of Motion, ArticularAdultFemaleHumansMaleMovementWeight-BearingYoung AdultBiomechanicsCartilageKinematics and kineticsKneeRehabilitation

Identifiers

PMID40634907
PMCPMC12239297

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.