A Novel approach to optimize neural network for classification of

EMG signals of different hand grasps

Mohammed Hanif Malpekar, Kalpesh Patil, Dr. A.K. Ray*, Dr. Nilanjan Mallik*

*Corresponding Author

This paper illustrates the classification of EMG signals of hand grasps namely

coin, cylinder, tripod and ball hand grasps through design and optimization of

Artificial neural networks by considering various combination of 2, 3 and 4 hand

grasps at its output, different hidden neurons and training data . ANN models of

different types are structured with many interconnected network elements with

multiple neurons in the intermediate layer simulating low level functions of

biological neurons which can develop pattern classification strategies based on set

of input/output training data. With comparison to traditional classifiers, ANN

models provide higher computational performance because they operate in parallel

compared to functioning sequentially. This paper describes a novel approach to

know the amount of training data and hidden neurons to be required for the

artificial neural network in achieving higher classification accuracy for classifying

different hand grasps. The EMG signals collected for different hand grasps, which

further conditioned by optimized EMG onset detection algorithm with window size

of 25 and then processed to extract temporal features such as Mean absolute value

(MAV), Zero crossings (ZC), Waveform length (WL) and Slope sign changes

(SSC). These extracted features are then used to train the artificial neural network.

Levenberg-Marquardt back propagation training algorithm has been used for

classification of EMG signals of different hand grasps. The results of this study

shows that the classification accuracy of the neural networks in classifying hand

grasps when considered 2, 3 and 4 hand grasps at its output has been increased by

increasing the training data within certain limit and also observed that the number

of hidden neurons to be used in the neural networks depend upon the number of

hand grasps to classify and training data.