Human activity classification with miniature inertial sensors

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

Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost

Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost