Kerem Altun

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). 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. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.

Human activity recognition using inertial/magnetic sensor units

This paper provides a comparative study on the different techniques of classifying human activities that are performed using bodyworn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM 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.

Evaluation of egocentric navigation methods

In describing routes humans make use of egocentric references to landmarks. In this paper two different egocentric navigation algorithms are introduced and possible variations of these algorithms are compared. These algorithms make use of solely angular distribution information of landmarks around the robot. Possible improvements and uses of these algorithms are discussed. In the evaluation of these algorithms computer simulations are used. The result of this work is expected to provide pointers for the ongoing research which aims to address navigation of robots based on qualitative interaction between humans and robots.

Performance Evaluation of Ultrasonic Arc Map Processing Techniques by Active Snake Contours

Active snake contours are considered for representing the maps of an environment obtained by different ultrasonic arc map (UAM) processing techniques efficiently. The mapping results are compared with the actual map of the room obtained with a very accurate laser system. This technique is a convenient way to represent and compare the map points obtained with different techniques among themselves, as well as with an absolute reference. It is also applicable to map points obtained with other mapping techniques.

Systematic State Equation Derivation for Linear Systems Using the Normal Tree Method

A systematic method to derive the state equations of a linear system starting from its linear graph is proposed. The normal tree is used in the analysis, which is a method to determine the dependencies between energy storage elements in the system. An algorithm to list all normal trees of a system graph is developed, which enables the determination of energy-based state variable sets and corresponding state equations. A computer program is developed to realize these algorithms, which derives the state equations of a system, given its linear graph.Copyright

Classification of leg motions by processing gyroscope signals

In this study, eight different leg motions are classified using two single-axis gyroscopes mounted on the right leg of a subject with the help of several pattern recognition techniques. The methods of least squares, Bayesian decision, k-nearest neighbor, dynamic time warping, artificial neural networks and support vector machines are used for classification and their performances are compared. This study comprises the preliminary work for our future studies on motion recognition with a much wider scope.