Evidence map›Paper›PMID 38743651›Full record

ArticlePLOS digital health2024

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.

Ricky Pimentel, Cortney Armitano-Lago, Ryan MacPherson, Anoop Sathyan, Jack Twiddy, Kaila Peterson, Michael Daniele, Adam W Kiefer, Edgar Lobaton, Brian Pietrosimone and 1 more

Abstract read
In one paragraph

Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Peak Vertical Ground Reaction Force Modifies Gait Biomechanics Bilaterally Following ACL Reconstruction: A Randomized Experiment.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025
    Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ricky PimentelJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill & North Carolina State University, Chapel Hill & Raleigh, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-8734-1996
Cortney Armitano-LagoDepartment of Exercise & Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0003-1412-5755
Ryan MacPhersonDepartment of Exercise & Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Anoop SathyanDepartment of Aerospace Engineering, University of Cincinnati, Cincinnati, OH, United States of America.
Jack TwiddyJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill & North Carolina State University, Chapel Hill & Raleigh, North Carolina, United States of America.ORCID https://orcid.org/0000-0003-1929-5796
Kaila PetersonDepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
Michael DanieleJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill & North Carolina State University, Chapel Hill & Raleigh, North Carolina, United States of America.
Adam W KieferDepartment of Exercise & Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Edgar LobatonDepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
Brian PietrosimoneDepartment of Exercise & Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Jason R FranzJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill & North Carolina State University, Chapel Hill & Raleigh, North Carolina, United States of America.ORCID https://orcid.org/0000-0001-9523-9708

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.

Identifiers

PMID38743651
PMCPMC11093287

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.