Gesture recognition plays an important role in human computer Interaction for intelligent computing. Major applications like Gaming, Robotics and Automated Homes uses gesture recognition techniques which diminishes the usage of mechanical devices. The main goal of my thesis is to interpret SWAT team gestures using different types of sensors. Accelerometer and flex sensors were explored extensively to build a prototype for soldiers to communicate in the absence of line of sight. Arm movements were recognized by flex sensors and motion gestures by Accelerometers. Accelerometers are used to measure acceleration in respect to movement of the sensor in 3D. Flex sensors changes its resistance based on the amount of bend in the sensor. SVM is the classification algorithm used for classification of the samples. LIBSVM (Library for Support Vector Machines) is integrated software for support vector classification, regression and distribution estimation which supports multi class classification. Sensors data is connected to the WI micro dig to digitize the signal and to transmit it wirelessly to the computing device. Feature extraction and Signal windowing were the two major factors which contribute for the accuracy of the system. Mean Average value and Standard Deviation are the two features considered for accelerometer sensor data classification and Standard deviation is used for the flex sensor analysis for optimum results. Filtering of the signal is done by identifying the different states of signals which are continuously sampled.

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