ANALYSIS OF INERTIAL SENSOR DATA USING TRAJECTORY RECOGNITION ALGORITHM

This paper describes a digital pen based on IMU sensor for gesture and handwritten digit gesture trajectory recognition applications. This project allows human and Pc interaction. Handwriting Recognition is mainly used for applications in the field of security and authentication. By using embedded pen the user can make hand gesture or write a digit and also an alphabetical character. The embedded pen contains an inertial sensor, microcontroller and a module having Zigbee wireless transmitter for creating handwriting and trajectories using gestures. The propound trajectory recognition algorithm constitute the sensing signal attainment, pre-processing techniques, feature origination, feature extraction, classification technique. The user hand motion is measured using the sensor and the sensing information is wirelessly imparted to PC for recognition. In this process initially excerpt the time domain and frequency domain features from pre-processed signal, later it performs linear discriminant analysis in order to represent features with reduced dimension. The dimensionally reduced features are processed with two classifiers – State Vector Machine (SVM) and k-Nearest Neighbour (kNN). Through this algorithm with SVM classifier provides recognition rate is 98.5% and with kNN classifier recognition rate is 95.5%