Ahmed Majid Abdel Abbas; Muayad Sadik Croock
Abstract
Recent advances in the control applications based on hand nervesignals are able to meet the needs of users who suffer from restrictions in limbmovement and also provide high performance control for those paralyzedpeople. These signals are represented as Electromyography (EMG) signals,which are biomedical ...
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Recent advances in the control applications based on hand nervesignals are able to meet the needs of users who suffer from restrictions in limbmovement and also provide high performance control for those paralyzedpeople. These signals are represented as Electromyography (EMG) signals,which are biomedical ones, used for clinical/biomedical applications. In thiswork, a control signals generation system is proposed based on hand EMGmeasurements. The process of acquisition and processing of EMG signals isperformed by only one channel surface EMG electrodes with one EMGprocessing unit as a muscle sensor. In this work, Arduino UNO is adopted as ananalog to digital converter for these hand nerve signals to be easily analyzed inthe classification process. These signals are measured from the skin surface offorearm and biceps muscles in two suggested case studies to be used ingenerating signals based on ten muscles movements. The main features thatcrystallized this research is building a smart control algorithm which increasesthe flexibility of generating precise control signals based on contracted handmovements with high simplicity of use and the low cost. The obtained results arecompared to other systems results to show the ability of achieving 93.81%classification rate or accuracy among other systems.
