ArticleSmart health (Amsterdam, Netherlands)2021
MyoMonitor: Evaluating muscle fatigue with commodity smartphones.
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.
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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.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Modern Smart Gadgets and Wearables for Diagnosis and Management of Stress, Wellness, and Anxiety: A Comprehensive Review.Healthcare (Basel, Switzerland) · 2025Review
- Environmental conditions, mobile digital culture, mobile usability, knowledge of app in COVID-19 risk mitigation: A structural equation model analysis.Smart health (Amsterdam, Netherlands) · 2022Article
- Pain and Stress Detection Using Wearable Sensors and Devices-A Review.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
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.