Hand Gesture Classification Using Emg Signal

The art of gesture recognition involves identification and classification of gestures. A gesture is any reproducible action or a sequence of actions. There are lots of techniques and algorithms to recognize gestures. In the project, gestures are recognized using biological signals generated by the human body. There are many biological signals that can be used for gesture recognition. Some of them are Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG). EMG signals are generally used because they have good signal strength (in the order of mV). Thus we use emg signal as the acquisition of EMG signals is easy and less complex ascompared to the above mentioned signals. Five different gestures such as Six features such as . root mean square, mean, standard deviation, variance, maximum and minimum values are extracted from the emg signals. The classifier used under the study is SVM , giving classification accuracy of 96.8%

Time Frequency Feature Extraction Scheme based on MUAP for classification of Neuromuscular Disorders using EMG signals.

The features of motor unit action potentials(MUAPs) are extracted from electromyographic (EMG) signals which provide information for diagnosis of neuromuscular disorders. Neuromuscular Disorders are classified into two categories Myopathic and Amyotrophic Lateral Sclerosis(ALS). ALS is a progressive neurodegenerative disease that affects nerve cells in the brain and the spinal cord. The progressive degeneration of the motor neurons in ALS eventually leads to their demise. When the motor neurons die, the ability of the brain to initiate and control muscle movement is lost hence the EMG signals of the patient of this disease are characterized by signals that have a increased value of amplitude , thereby increasing the peak to peak value of the signal. On the other hand Myopathies are a group of disorders characterized by a primary structural or functional impairment of skeletal muscle. They usually affect muscle without involving the nervous system, resulting in muscular weakness hence the EMG signals of the patients of this group of disorder are characterized by signals of shorter duration and smaller amplitude. The aim of this study, is to design a automated system which can classify the signals as ALS , Myopathic and Normal.The proposed scheme employs extracting both time and time–frequency features of a MUAP and then providing it to classifier which can classify the signals as ALS, myopathic and normal.In the proposed system, three classifiers are implemented and their results are evaluated out of which Random Forest classification technique provides the highest accuracy of 97.85%