پديدآورندگان :
Karimpour M Shiraz University of Medical Sciences , Sharifian R Shiraz University of Medical Sciences , Parsaei H Shiraz University of Medical Sciences , Rojhani Z Shiraz University of Medical Sciences , Yazdani F Shiraz University of Medical Sciences
كليدواژه :
Design , Implement , Analysis , Electromyography , Onset Latency , Android.
چكيده فارسي :
Background: Advances in medical science and technology has caused to provide effective methods for diagnosis and treatment of diseases and injuries. One of these methods is mobile health. Mobile health is part of E-Health which includes the health information services through mobile phone technology. One of these practical uses of mobile health technology, is its use in physiological monitoring. Physiological monitoring is called reading, storing, processing and interpreting the physiological data of organisms, including biofeedback signals to the heart, brain, muscle and other organs activity. electromyography is a sample of physiological data. Electromyography processing and Muscle onset latency are used in Rehabilitation Sciences and nerve conduction studies. Studies show that most software offered in mobile health and Rehabilitation Sciences are related to training exercises to improve muscle function, or games to improve mental and physical abilities. The need for portable applications such as smart phones or tablet devices for physiological processes within the electromyography and estimates the Muscle onset latency from the professionals of Rehabilitation Sciences can be felt especially in Iran, thus design and implementation of the electromyography analysis software under the Android operating system in order to estimate the Muscle Onset Latency, can be a new version of software for mobile phone applications in the field of rehabilitation.
Objectives: The aim of this study is to provide an android software in order to estimating muscle onset latency.
Methods: In this study, first of all by using scientific literature of pre-processing algorithms and the Muscle onset latency and affecting factors in Muscle Onset Latency, are identified in database design and by using flowcharts design tool and database are designed logically and afterward are implemented in software.
Results: The findings of this study includes preprocessing algorithm flowchart design and Muscle onset latency under Android operating system, internal and external database design software, user Interface Design, software design interfaces to communicate with the electromyography, design of sharing software output system.
Conclusion: According to the results of this study, estimation of Muscle onset latency is used in nerve conduction studies including velocity of nerve conduction and by considering the increasing use of the Android OS in smart portable devices such as mobile phones and tablets, this feature gives the mentioned plan, the ability to implement and use this software on mobile phones or tablets by most of the users.
