Yüksek, Murat Cihan2016-01-082016-01-082011http://hdl.handle.net/11693/15616Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 57-67.This study provides a comparative assessment on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques compared in this study are: naive Bayesian (NB) classifier, artificial neural networks (ANNs), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). The algorithms for these techniques are provided on two commonly used open source environments: Waikato environment for knowledge analysis (WEKA), a Java-based software; and pattern recognition toolbox (PRTools), a MATLAB toolbox. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost. The methods that result in the highest correct differentiation rates are found to be ANN (99.2%), SVM (99.2%), and GMM (99.1%). The magnetometer is the best type of sensor to be used in classification whereas gyroscope is the least useful. Considering the locations of the sensor units on body, the sensors worn on the legs seem to provide the most valuable information.xv, 67 leaves, illustrationsEnglishinfo:eu-repo/semantics/openAccessinertial sensorsgyroscopeaccelerometermagnetometeractivity recognition and classificationfeature extraction and reductioncross validationBayesian decision makingartificial neural networkssupport vector machinesdecision treesdissimilarity-based classifierGaussian mixture modelWEKAPRToolsTA1650 .Y85 2011Optical pattern recognition.Computer vision.Image processing--Digital techniques.Body, Human--Computer simulation.Sensors.Human locomotion.Intelligent control systems.Detectors.Thesis