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.
Background and Objectives: Cerebral Palsy (CP) can trouble caregivers in the families of children with cerebral palsy. This study aimed to investigate the factors affecting caregiver troubles of the families of children with CP. Methods: In this cross-sectional study, 121 children with CP and their parents participated. Factors such as gross motor function, manual ability, communication function, eating and drinking abilities, seizure, IQ, age, gender, auditory and visual problems were evaluated in children, and their predictive power to the caregiver difficulties was measured. Results: The Mean±SD of age of the children in the study was 9.7±4.6 years. The linear regression results showed that none of the evaluated factors were predictors of caregiver difficulties. Conclusion: The present study showed that none of the factors mentioned could predict caregivers’ troubles. Future studies on the factors affecting the caregiver difficulty should be conducted to provide additional information or to draw more complex models to describe caregivers’ difficulties in parents of children with CP in Iran.
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