Evidence map›Paper›PMID 35408112›Full record

ArticleSensors (Basel, Switzerland)2022

Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors.

David S Wood, Kurt Jensen, Allison Crane, Hyunwook Lee, Hayden Dennis, Joshua Gladwell, Anne Shurtz, David T Fullwood, Matthew K Seeley, Ulrike H Mitchell and 2 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Bending Angle Sensor Based on Double-Layer Capacitance Suitable for Human Joint.IEEE open journal of engineering in medicine and biology · 2023
    Article
  6. Technological advances in lower-limb tele-rehabilitation: A review of literature.Journal of rehabilitation and assistive technologies engineering
    Review
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

12 authors.

David S WoodDepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0002-6643-1618
Kurt JensenDepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.
Allison CraneDepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.
Hyunwook LeeDepartment of Exercise Science, Brigham Young University, Provo, UT 84602, USA.
Hayden DennisDepartment of Exercise Science, Brigham Young University, Provo, UT 84602, USA.
Joshua GladwellDepartment of Statistics, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0001-8812-9383
Anne ShurtzDepartment of Statistics, Brigham Young University, Provo, UT 84602, USA.
David T FullwoodDepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0002-7486-3672
Matthew K SeeleyDepartment of Exercise Science, Brigham Young University, Provo, UT 84602, USA.
Ulrike H MitchellDepartment of Exercise Science, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0002-2298-8756
William F ChristensenDepartment of Statistics, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0002-8068-1031
Anton E BowdenDepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.ORCID 0000-0003-3124-7551

Funding

National Science Foundation CBET-1901845
6 · The paper itself

Abstract

In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device is presented. Testing was performed on eighteen subjects, and knee angles were predicted using a machine learning regressor. Subject-specific and device-specific models are analyzed and presented. Subject-specific models average root mean square errors of 7.6 and 1.8 degrees for flexion/extension and internal/external rotation, respectively. Device-specific models average root mean square errors of 12.6 and 3.5 degrees for flexion/extension and internal/external rotation, respectively. The device presented in this work proved to be a repeatable, reusable, low-cost device that can adequately model the knee's flexion/extension and internal/external rotation angles for rehabilitation purposes.

Indexed as

NanocompositesWearable Electronic DevicesBiomechanical PhenomenaExercise TherapyHumansKnee JointRange of Motion, Articularnanocomposite stretch sensorsrehabilitationsmart textile

Identifiers

PMID35408112
PMCPMC9003122

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.