Browsing by Author "Altun, Kerem"

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Open Access
Classification of leg motions by processing gyroscope signals
(IEEE, 2009) Tunçel, Orkun; Altun, Kerem; Barshan, Billur
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.
Open Access
Directional processing of ultrasonic arc maps and its comparison with existing techniques
(IEEE, 2007) Barshan, Billur; Altun, Kerem
Directional maximum (DM) technique for processing ultrasonic arc maps (UAMs) is proposed and compared to existing techniques. It employs directional processing in extracting the map of the environment from UAMs. DM aims at overcoming the intrinsic angular uncertainty of ultrasonic sensors in map building, as well as eliminating noise and cross-talk related misreadings. The comparison is based on experiments with a mobile robot which ac-quired ultrasonic range measurements through wall following. Three complementary performance criteria are used. The DM technique offers a very good compromise between mean absolute error and correct detection rate, with a processing time less than one tenth of a second. It is also superior in range accuracy and in eliminating artifacts, resulting in the best overall performance. The results indicate several trade-offs in the choice of UAM processing techniques.
Open Access
Employing active contours and artificial neural networks in representing ultrasonic range data
(IEEE, 2008-08) Altun, Kerem; Barshan, Billur
Active snake contours and Kohonen's self-organizing feature maps (SOM) are considered for efficient representation and evaluation of the maps of an environment obtained with different ultrasonic arc map (UAM) processing techniques. The mapping results are compared with a reference map acquired with a very accurate laser system. Both approaches are convenient ways of representing and comparing the map points obtained with different techniques among themselves, as well as with an absolute reference. Snake curve fitting results in more accurate maps than SOM since it is more robust to outliers. The two methods are sufficiently general that they can be applied to discrete point maps acquired with other mapping techniques and other sensing modalities as well. copyright by EURASIP.
Open Access
Human activity recognition using inertial/magnetic sensor units
(Springer, Berlin, Heidelberg, 2010) Altun, Kerem; Barshan, Billur
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), 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. © 2010 Springer-Verlag Berlin Heidelberg.
Open Access
Intelligent sensing for robot mapping and simultaneous human localization and activity recognition
(2011) Altun, Kerem
We consider three different problems in two different sensing domains, namely ultrasonic sensing and inertial sensing. Since the applications considered in each domain are inherently different, this thesis is composed of two main parts. The approach common to the two parts is that raw data acquired from simple sensors is processed intelligently to extract useful information about the environment. In the first part, we employ active snake contours and Kohonen’s selforganizing feature maps (SOMs) for representing and evaluating discrete point maps of indoor environments efficiently and compactly. We develop a generic error criterion for comparing two different sets of points based on the Euclidean distance measure. The point sets can be chosen as (i) two different sets of map points acquired with different mapping techniques or different sensing modalities, (ii) two sets of fitted curve points to maps extracted by different mapping techniques or sensing modalities, or (iii) a set of extracted map points and a set of fitted curve points. The error criterion makes it possible to compare the accuracy of maps obtained with different techniques among themselves, as well as with an absolute reference. We optimize the parameters of active snake contours and SOMs using uniform sampling of the parameter space and particle swarm optimization. A demonstrative example from ultrasonic mapping is given based on experimental data and compared with a very accurate laser map, considered an absolute reference. Both techniques can fill the erroneous gaps in discrete point maps. Snake curve fitting results in more accurate maps than SOMs because it is more robust to outliers. The two methods and the error criterion are sufficiently general that they can also be applied to discrete point maps acquired with other mapping techniques and other sensing modalities. In the second part, we use body-worn inertial/magnetic sensor units for recognition of daily and sports activities, as well as for human localization in GPSdenied environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors are modeled using the Allan variance technique, and the parameters of lowand high-frequency error components are estimated. Then, we provide a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. We compute a large number of features extracted from the sensor data, and reduce these features using both Principal Components Analysis (PCA) and sequential forward feature selection (SFFS). We consider eight different pattern recognition techniques and provide a comparison in terms of the correct classification rates, computational costs, and their training and storage requirements. Results with sensors mounted on various locations on the body are also provided. The results indicate that if the system is trained by the data of an individual person, it is possible to obtain over 99% correct classification rates with a simple quadratic classifier such as the Bayesian decision method. However, if the training data of that person are not available beforehand, one has to resort to more complex classifiers with an expected correct classification rate of about 85%. We also consider the human localization problem using body-worn inertial/ magnetic sensors. Inertial sensors are characterized by drift error caused by the integration of their rate output to get position information. Because of this drift, the position and orientation data obtained from inertial sensor signals are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user provides information about position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing activity recognition simultaneously with localization, one can detect the activity context switches and use the corresponding position information as position updates in the localization filter. The localization filter also involves a smoother, which combines the two estimates obtained by running the zero-velocity update (ZUPT) algorithm both forward and backward in time. We performed experiments with eight subjects in an indoor and an outdoor environment involving “walking,” “turning,” and “standing” activities. Using the error criterion in the first part of the thesis, we show that the position errors can be decreased by about 85% on the average. We also present the results of a 3-D experiment performed in a realistic indoor environment and demonstrate that it is possible to achieve over 90% error reduction in position by performing activity recognition simultaneously with localization.