Aiming at the use of hand gestures for human-computer interaction, this paper presents an approach to identify hand gestures using muscle activity separated from electromyogram (EMG) using independent component analysis. While there are a number of previous reported works where EMG has been used to identify movement, the limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The paper reports experimental results where the system was accurately able to identify the hand gesture using this technique for all the experiments (100%). The system has been shown not to be sensitive to electrode position as the experiments were repeated on different days. The advantage of such a system is that it is easy to train by a lay user, and can easily be implemented in real time after the initial training.

This paper examines the use of facial muscle activity (Surface Electromyogram) to recognise speech based commands in English and German language without any audio signals. The system is designed for applications based on speech control for human computer interaction (HCI). The paper presents an effective technique that uses the facial muscle activity of the articulatory muscles and human factors for recognition. The difference in the speed and style of speaking varies between experiments, and this variation appears to be more pronounced when people are speaking a foreign language. To overcome this difficulty, the paper reports measuring the relative activity of the articulatory muscles for recognition of unvoiced vowels of English and German languages. In these investigations, three English vowels and three German vowels were used as recognition variables. The moving root mean square (RMS) of surface electromyo-gram (SEMG) of four facial muscles is used to segment the signal and to identify thestart and end of a silently spoken utterance. The relative muscle activity is computed by integrating and normalising the RMS values of the signals between the detected start and end markers. The output vector of this is classified using a back propagation neural network to identify the voiceless speech. The data is also tested using K means clustering technique to determine the linearity of separation of the data. The experimental results show that this technique gives high recognition rate when used for each of the participants for both of the languages. The investigations also show that the system is easy to train for a new user. The visual inspection of the plot of the experimental data suggests the formation of clusters. The results suggest that such a system is reliable for simple vowel based commands for human computer interface when it is trained for the user, who can speak one or more languages and for the people who have speech disability.