A comparison of different approaches to target differentiation with sonar

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Ph.D.) -- Bilkent University, 2001.Includes bibliographical references leaves 180-197This study compares the performances of di erent classication schemes and fusion techniques for target di erentiation and localization of commonly encountered features in indoor robot environments using sonar sensing Di erentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identication map building navigation obstacle avoidance and target tracking The classication schemes employed include the target di erentiation algorithm developed by Ayrulu and Barshan statistical pattern recognition techniques fuzzy c means clustering algorithm and articial neural networks The fusion techniques used are Dempster Shafer evidential reasoning and di erent voting schemes To solve the consistency problem arising in simple ma jority voting di erent voting schemes including preference ordering and reliability measures are proposed and veried experimentally To improve the performance of neural network classiers di erent input signal representations two di erent training algorithms and both modular and non modular network structures are considered The best classication and localization scheme is found to be the neural network classier trained with the wavelet transform of the sonar signals This method is applied to map building in mobile robot environments Physically di erent sensors such as infrared sensors and structured light systems besides sonar sensors are also considered to improve the performance in target classication and localization.Ayrulu (Erdem), BirselPh.D

Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform

Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided

Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit.

Monitoring the performance is a crucial task for elite sports during both training and competition. Velocity is the key parameter of performance in swimming, but swimming performance evaluation remains immature due to the complexities of measurements in water. The purpose of this study is to use a single inertial measurement unit (IMU) to estimate front crawl velocity. Thirty swimmers, equipped with an IMU on the sacrum, each performed four different velocity trials of 25 m in ascending order. A tethered speedometer was used as the velocity measurement reference. Deployment of biomechanical constraints of front crawl locomotion and change detection framework on acceleration signal paved the way for a drift-free integration of forward acceleration using IMU to estimate the swimmers velocity. A difference of 0.6 ± 5.4 cm · s(-1) on mean cycle velocity and an RMS difference of 11.3 cm · s(-1) in instantaneous velocity estimation were observed between IMU and the reference. The most important contribution of the study is a new practical tool for objective evaluation of swimming performance. A single body-worn IMU provides timely feedback for coaches and sport scientists without any complicated setup or restraining the swimmer's natural technique