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Karimpour, M, Parsaei, H, Rojhani-Shirazi, Z, Sharifian, R, Yazdani, F. (1398). An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal. سامانه مدیریت نشریات علمی, 9(2), 243-250. doi: 10.31661/jbpe.v0i0.700
M Karimpour; H Parsaei; Z Rojhani-Shirazi; R Sharifian; F Yazdani. "An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal". سامانه مدیریت نشریات علمی, 9, 2, 1398, 243-250. doi: 10.31661/jbpe.v0i0.700
Karimpour, M, Parsaei, H, Rojhani-Shirazi, Z, Sharifian, R, Yazdani, F. (1398). 'An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal', سامانه مدیریت نشریات علمی, 9(2), pp. 243-250. doi: 10.31661/jbpe.v0i0.700
Karimpour, M, Parsaei, H, Rojhani-Shirazi, Z, Sharifian, R, Yazdani, F. An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal. سامانه مدیریت نشریات علمی, 1398; 9(2): 243-250. doi: 10.31661/jbpe.v0i0.700

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

Journal of Biomedical Physics and Engineering
مقاله 13، دوره 9، شماره 2، خرداد و تیر 2019، صفحه 243-250    اصل مقاله (575.62 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.700
نویسندگان
M Karimpour1، 2؛ H Parsaei3، 4؛ Z Rojhani-Shirazi5؛ R Sharifian* 1؛ F Yazdani5
1School of Management & Medical Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
2Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran
3Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
4Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
5Department of Physiotherapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
چکیده
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.
Conclusion: 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.
کلیدواژه‌ها
Electromyography؛ Surface EMG signal analysis؛ Muscle Onset Latency؛ Muscle Onset Latency Estimation؛ Android application
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