Abstract
Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
Highlights
EMG signal is one of the main signals produced by the human body especially by the muscles
EMG signal is widely used in many applications recently
EMG signal used for human-machine interface
Summary
EMG signal is one of the main signals produced by the human body especially by the muscles. The results of electromyography are nonspecific electromyography is very sensitive [1]. EMG signal is widely used in many applications recently. The most active area of this application is prosthesis hand control. EMG has advantages compare to other biological signals. Because EMG signals are powerful and have high signal to noise ratio
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