An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

Abstract

Background: Electromyography (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.Objective: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG.Material and Methods: A 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.Results: Evaluation 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.Conclusions: Recent 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.

Effects of Plyometric Jump Training on Electromyographic Activity and Its Relationship to Strength and Jump Performance in Healthy Trained and Untrained Populations: A Systematic Review of Randomized Controlled Trials

“…Therefore, regardless of sex, it is striking that most of the muscle activation outcomes analyzed in the studies included in this review demonstrated no significant changes. It is possible that certain limitations related to assessment techniques (e.g., sensitivity of the equipment used to measure sEMG, use of portable vs. nonportable equipment, adequate selection of muscles type of action, lack of multiple surface electromyograms, and dynamic vs. isometric actions), among others (28,32,49,55,59,77,85,86), may help to explain the high prevalence of muscle activation outcomes not responsive to PJT. Furthermore, the sEMG-force relationship is best described as curvilinear with a plateau starting at the near maximal force range (8,68), which diminished its sensitivity to change with maximal or near maximal contractions.…”

“…It is possible that other adaptations, such as mechanical (e.g., musculotendinous stiffness) (87), motor skill (e.g., joint angle and intermuscular coordination) (3), anatomical (e.g., muscle fiber pennation angle) (59), or hypertrophy-related (37), may help to explain the improvements in physical fitness after PJT when muscle activation outcomes displayed no significant changes. Alternatively, certain limitations related to assessment techniques, such as the sensitivity of the equipment used to measure sEMG, inadequate selection of muscles type or action, lack of multiple surface electromyograms, and among others (28,49,55,59,77,85,86), may also help to explain the commonly observed high proportion of sEMG-related measures nonresponsive to PJT. In addition, nonoptimal PJT prescription might be a potential reason for the high prevalence of nonresponsive muscle activation outcomes.…”

Effect of Progressive Muscle Relaxation Exercise Based on Arduino Uno on Blood Sugar, Cholesterol and Urid Acid Levels in the Elderly

“…This study uses a demographic data questionnaire and a muscle strength sensor observation sheet based on Arduino Uno with an Android application (Karimpour, Parsaei, Rojhani, Sharifian, & Yazdani, 2019). This study uses a demographic data questionnaire and an Arduino Uno based power sensor observation sheet with an Android application.…”

Effect of a Developed Nursing Stretch Break Application on Work-Related Musculoskeletal Complications and Fatigue among Nurses: An Interventional Study

“…Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost, particularly in academic environments. Also, performance is promising to use the app for teaching purposes [ 11 ]. Chhabra et al in their study claimed that health applications were promising tools for improving outcomes in patients suffering from various chronic conditions.…”