ArticleJournal of biomedical physics & engineering2019
An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal.
Article in Journal of biomedical physics & engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Electromyographic Activity of Cervical Muscles in Patients with Neck Pain and Changes After Dry Needling: A Narrative Review.Journal of clinical medicine · 2024Review
- Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction.Scientific reports · 2023Article
- Development of a Mobile-Based Self-care Application for Patients with Breast Cancer-Related Lymphedema in Iran.Applied clinical informatics · 2022Article
- Effect of a Developed Nursing Stretch Break Application on Work-Related Musculoskeletal Complications and Fatigue among Nurses: An Interventional Study.Pain research & management · 2022Article
- Winter School on sEMG Signal Processing: An Initiative to Reduce Educational Gaps and to Promote the Engagement of Physiotherapists and Movement Scientists With Science.Frontiers in neurology · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs.
objectiveDevelop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG. MATERIAL AND
methodsA multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too.
resultsEvaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL.
conclusionRecent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes.
Indexed as
Identifiers
31214530PMC6538912What OpenQuestion holds
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.