Multi modal gesture identification for HCI using surface EMG

Gesture and Speech comprise the most important modalities of human interaction. There has been a considerable amount of research attempts at incorporating these modalities for natural HCI. This involves challenge ranging from the low level signal processing of multi-modal input to the high level interpretation of natural speech and gesture in HCI. This paper proposes novel methods to recognize the hand gestures and unvoiced utterances using surface Electromyogram (sEMG) signals originating from different muscles. The focus of this work is to establish a simple, yet robust system that can be integrated to identify subtle complex hand gestures and unvoiced speech commands for control of prosthesis and other computer assisted devices. The proposed multi-modal system is able to identify the hand gestures and silent utterances using Independent Component Analysis (ICA) and Integral RMS (IRMS) of sEMG respectively. Training of the sEMG features was done using a designed ANN architecture and the results reported with overall recognition accuracy of 90.33%

Iterative issues of ICA, quality of separation and number of sources : a study for biosignal applications

This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks