Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 79-92.This thesis provides a comparative study on activity recognition using miniature
inertial sensors (gyroscopes and accelerometers) and magnetometers worn
on the human body. The classification methods used and compared in this
study are: a rule-based algorithm (RBA) or decision tree, least-squares method
(LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW-
1 and DTW-2), and support vector machines (SVM). In the first part of this
study, eight different leg motions are classified using only two single-axis gyroscopes.
In the second part, human activities are classified using five sensor units
worn on different parts of the body. Each sensor unit comprises a tri-axial gyroscope,
a tri-axial accelerometer and a tri-axial magnetometer. Different feature
sets extracted from the raw sensor data and these are used in the classification
process. A number of feature extraction and reduction techniques (principal
component analysis) as well as different cross-validation techniques have been
implemented and compared. A performance comparison of these classification
methods is provided in terms of their correct differentiation rates, confusion matrices,
pre-processing and training times and classification times. Among the
classification techniques we have considered and implemented, SVM, in general,
gives the highest correct differentiation rate, followed by k-NN. The classification
time for RBA is the shortest, followed by SVM or LSM, k-NN or DTW-1,
and DTW-2 methods. SVM requires the longest training time, whereas DTW-2
takes the longest amount of classification time. Although there is not a significant
difference between the correct differentiation rates obtained by different crossvalidation
techniques, repeated random sub-sampling uses the shortest amount
of classification time, whereas leave-one-out requires the longest.Tunçel, OrkunM.S
