Evidence map›Paper›PMID 35925858›Full record

ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2022

Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring.

Jonathan T Alvarez, Lucas F Gerez, Oluwaseun A Araromi, Jessica G Hunter, Dabin K Choe, Christopher J Payne, Robert J Wood, Conor J Walsh

Abstract read
In one paragraph

Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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.

Jonathan T Alvarez
Lucas F Gerez
Oluwaseun A Araromi
Jessica G Hunter
Dabin K Choe
Christopher J Payne
Robert J Wood
Conor J Walsh

Funding

ENGINEERING SKELETAL MUSCLE WITH BIODEGRADABLE HYDROGELSR01DE013349 · NIDCR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MOONEY, DAVID J · 2000 to 2022
$9.3M
NIDCR NIH HHS R01 DE013349
6 · The paper itself

Abstract

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients' recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson's disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors' sensitivity to isometric contractions, as well as the sensors' capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03 ).

Indexed as

Isometric ContractionWearable Electronic DevicesHumansKnee JointMuscle FatigueMuscle, SkeletalMuscle StrengthTorque

Identifiers

PMID35925858
PMCPMC9421605

What OpenQuestion holds

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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.