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

Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_JBPE-9-2_013

تاریخ نمایه سازی: 30 دی 1402

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 ۱۰ 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 ۳.۰). For the majority of cases relative error <۱۰%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ ۰.۹۳. These evaluations revealed that the presented app performed as well as MegaWin ۳.۰ 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.

Keywords:

Electromyography , Surface EMG signal analysis , Muscle Onset Latency , Muscle Onset Latency Estimation , Android application

Authors

M Karimpour

School of Management & Medical Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

H Parsaei

Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Z Rojhani-Shirazi

Department of Physiotherapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

R Sharifian

School of Management & Medical Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

F Yazdani

Department of Physiotherapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

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  • Laal M. Technology in medical science. Procedia-Social and Behavioral Sciences. ...
  • O’Sullivan T, Studdert R, editors. Handheld medical devices negotiating for ...
  • Kay M, Santos J, Takane M. mHealth: New horizons for ...
  • Meneghello J, Lee K, Gilleade K, editors. Mobile distributed processing ...
  • Adibi S. Mobile health: a technology road map. New York: ...
  • K TH, A BB, Garan H, Sciacca RR, Riga T, ...
  • Secerbegovic A, Mujčić A, Suljanović N, Nurkic M, Tasic J, ...
  • Stalberg E, Falck B. The role of electromyography in neurology. ...
  • Brannagan TH, Hays AP, Lange DJ, Trojaborg W. The role ...
  • Fuglsang-Frederiksen A. The role of different EMG methods in evaluating ...
  • Farkas C, Hamilton-Wright A, Parsaei H, Stashuk DW. A review ...
  • Hodges PW, Bui BH. A comparison of computer-based methods for ...
  • Oh SJ. Clinical electromyography: nerve conduction studies. Philadelphia: Lippincott Williams ...
  • Kimura J. Electrodiagnosis in diseases of nerve and muscle. Oxford: ...
  • Nikolic M, Krarup C. EMGTools, an adaptive and versatile tool ...
  • Stalberg E, Falck B, Sonoo M, Stalberg S, Astrom M. ...
  • Farina D, Fattorini L, Felici F, Filligoi G. Nonlinear surface ...
  • Tomberg C, Levarlet-Joye H, Desmedt JE. Reaction times recording methods: ...
  • Di Fabio RP. Reliability of computerized surface electromyography for determining ...
  • Parsaei H, Stashuk DW, Rasheed S, Farkas C, Hamilton-Wright A. ...
  • Parsaei H, Stashuk DW. EMG signal decomposition using motor unit ...
  • Ozgunen KT, Celik U, Kurdak SS. Determination of an Optimal ...
  • Holla S, Katti MM. Android based mobile application development and ...
  • Henriques G, Lamanna L, Kotowski D, Hlomani H, Stacey D, ...
  • Lee Y, Ashton-Miller JA. Age and gender effects on the ...
  • Pressman RS, Maxim B. Software engineering: a practitioner’s approach. ۸th ...
  • Jinjin H, Zhaolin F. The design of ERP in the ...
  • Alam MM, Hati S, De D, Chattopadhyay S, editors. Secure ...
  • Bhati S, Sharma S, Singh K. Review On Google Android ...
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