Evidence map›Paper›PMID 36338262›Full record

ArticleSmart health (Amsterdam, Netherlands)2021

MyoMonitor: Evaluating muscle fatigue with commodity smartphones.

Xingzhe Song, Hongshuai Li, Wei Gao

Open access · greenAbstract read
In one paragraph

Article in Smart health (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

3 authors at 1 institution in 1 country.

Xingzhe SongDepartment of Electrical and Computer Engineering, University of Pittsburgh, USA.
Hongshuai LiDepartment of Orthopaedic Surgery, University of Pittsburgh, USA.
Wei GaoDepartment of Electrical and Computer Engineering, University of Pittsburgh, USA.
University of Pittsburgh · US

Funding

Targeting Nitrate-Nitrite-NO pathway for Ameliorating Muscle and Bone Comorbidities in Duchenne Muscular DystrophyR01AR076357 · NIAMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LI, HONGSHUAI · 2020 to 2024
$1.6M
NIAMS NIH HHS R01 AR076357
6 · The paper itself

Abstract

Muscle fatigue is common among humans and also a crucial indicator of many muscular diseases such as muscular dystrophy and disorders. Timely evaluation of muscle fatigue, hence, is important to track disease progress and avoid disease exacerbations. However, convenient tools for evaluating muscle fatigue out of clinic are still missing. In this paper, we present a new technique that uses commodity smartphones to evaluate muscle fatigue through simple and daily muscle exercises. The basic idea of our technique is to mimic an active sonar system with the smartphone's built-in microphone and speaker, and use this sonar system to evaluate muscle fatigue from the muscle's surface characteristics that can be measured from the transmitted acoustic signal. More specifically, our technique first measures the acoustic channel disturbances caused by fatigue-induced muscle tremor via channel estimation, and then derives quantitative fatigue levels from the variation of acoustic channel estimation. By using the arm bicep muscle as our primary target, we designed the exercise protocol and implemented a smartphone app for fatigue evaluation. Experiment results verified that our technique can precisely evaluate the speed of muscle fatigue accumulation, as well as identifying the actual fatigue occurrence. This technique, hence, could be used in practical home settings for effective fatigue evaluation on a daily basis.

Identifiers

PMID36338262
PMCPMC9632324
OpenAlexW3111484774

What OpenQuestion holds

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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.