Evidence map›Paper›PMID 32903362›Full record

ArticleBMC biomedical engineering2020

Human motion component and envelope characterization via wireless wearable sensors.

Kaitlyn R Ammann, Touhid Ahamed, Alice L Sweedo, Roozbeh Ghaffari, Yonatan E Weiner, Rebecca C Slepian, Hongki Jo, Marvin J Slepian

Open access · goldAbstract read
In one paragraph

Article in BMC biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 34% of its field
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

4 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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  3. Review
  4. 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

8 authors at 3 institutions in 1 country.

Kaitlyn R AmmannDepartment of Medicine, University of Arizona, Tucson, AZ USA.
Touhid AhamedDepartment of Civil Engineering, University of Arizona, Tucson, AZ USA.
Alice L SweedoDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ USA.
Roozbeh GhaffariDepartment of Biomedical Engineering, Northwestern University, Evanston, IL USA.
Yonatan E WeinerDepartment of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA USA.
Rebecca C SlepianDepartment of Medicine, University of Arizona, Tucson, AZ USA.
Hongki JoDepartment of Civil Engineering, University of Arizona, Tucson, AZ USA.
Marvin J SlepianDepartment of Medicine, University of Arizona, Tucson, AZ USA.ORCID 0000-0002-7864-6691
University of Arizona · USNorthwestern University · USWorcester Polytechnic Institute · US

Funding

Cardiovascular Biomedical Engineering Training GrantT32HL007955 · NHLBI · UNIVERSITY OF ARIZONA · PI BARTON, JENNIFER KEHLET · 2001 to 2020
$5.1M
NHLBI NIH HHS T32 HL007955
6 · The paper itself

Abstract

backgroundThe characterization of limb biomechanics has broad implications for analyzing and managing motion in aging, sports, and disease. Motion capture videography and on-body wearable sensors are powerful tools for characterizing linear and angular motions of the body, though are often cumbersome, limited in detection, and largely non-portable. Here we examine the feasibility of utilizing an advanced wearable sensor, fabricated with stretchable electronics, to characterize linear and angular movements of the human arm for clinical feedback. A wearable skin-adhesive patch with embedded accelerometer and gyroscope (BioStampRC, MC10 Inc.) was applied to the volar surface of the forearm of healthy volunteers. Arms were extended/flexed for the range of motion of three different regimes: 1) horizontal adduction/abduction 2) flexion/extension 3) vertical abduction. Data were streamed and recorded revealing the signal "pattern" of movement in three separate axes. Additional signal processing and filtering afforded the ability to visualize these motions in each plane of the body; and the 3-dimensional motion envelope of the arm.

resultsEach of the three motion regimes studied had a distinct pattern - with identifiable qualitative and quantitative differences. Integration of all three movement regimes allowed construction of a "motion envelope," defining and quantifying motion (range and shape - including the outer perimeter of the extreme of motion - i.e. the envelope) of the upper extremity. The linear and rotational motion results from multiple arm motions match measurements taken with videography and benchtop goniometer.

conclusionsA conformal, stretchable electronic motion sensor effectively captures limb motion in multiple degrees of freedom, allowing generation of characteristic signatures which may be readily recorded, stored, and analyzed. Wearable conformal skin adherent sensor patchs allow on-body, mobile, personalized determination of motion and flexibility parameters. These sensors allow motion assessment while mobile, free of a fixed laboratory environment, with utility in the field, home, or hospital. These sensors and mode of analysis hold promise for providing digital "motion biomarkers" of health and disease.

Indexed as

AccelerometersBiomechanicsBiomedical measurementBiometricsBiosensorsBiotechnologyEngineering in medicine and biologyGyroscopesMotion analysisWearable sensors

Identifiers

PMID32903362
PMCPMC7422588
OpenAlexW3031055318

What OpenQuestion 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 OpenQuestion graph.