Browsing by Subject "Gyroscopes"
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Item Open Access Activity recognition invariant to sensor orientation with wearable motion sensors(MDPI AG, 2017) Yurtman, A.; Barshan, B.Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.Item Open Access Classification of leg motions by processing gyroscope signals(IEEE, 2009) Tunçel, Orkun; Altun, Kerem; Barshan, BillurIn 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.Item Open Access Classifying human leg motions with uniaxial piezoelectric gyroscopes(2009) Tunçel O.; Altun, K.; Barshan, B.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. © 2009 by the authors.Item Open Access Evaluation of solid-state gyroscope for robotics applications(Institute of Electrical and Electronics Engineers, 1995-02) Barshan, B.; Durrant-Whyte, H. F.he evaluation of a low-cost solid-state gyroscope for robotics applications is described. An error model for the sensor is generated and included in a Kalman filter for estimating the orientation of a moving robot vehicle. Orientation eshation with the error model is compared to the performance when the error model is excluded from the system. The results demonstrate that without error compensation, the error in localization is between 5-15"/min but can be improved at least by a factor of 5 if an adequate error model is supplied. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, inertial sensors can provide valuable orientation information for mobile robot applications.Item Open Access Fizik tedavi egzersizlerinin giyilebilir hareket algılayıcıları işaretlerinden dinamik zaman bükmesiyle sezimi ve değerlendirilmesi(IEEE, 2014-04) Yurtman, Aras; Barshan, BillurGiyilebilir hareket algılayıcılarından kaydedilen sinyalleri işleyerek fizik tedavi egzersizlerini algılamak ve değerlendirmek için özerk bir sistem geliştirilmiştir. Bir fizik tedavi seansındaki bir ya da birden fazla egzersiz tipini algılamak için, temeli dinamik zaman bükmesi (DZB) benzeşmezlik ölçütüne dayanan bir algoritma geliştirilmiştir. Algoritma, egzersizlerin doğru ya da yanlış yapıldığını değerlendirmekte ve varsa hata türünü saptamaktadır. Algoritmanın başarımını degerlendirmek için, beş katılımcı tarafından yapılan sekiz egzersiz hareketinin üç yürütüm türü için birer şablon ve 10’ar sınama yürütümünden oluşan bir veri kümesi kaydedilmiştir. Dolayısıyla, eğitim ve sınama kümelerinde sırasıyla 120 ve 1,200 egzersiz yürütümü bulunmaktadır. Sınama kümesi, boş zaman dilimleri de içermektedir. Öne sürülen algoritma, sınama kümesindeki 1,200 yürütümün % 8.58’ini kaçırmakta ve boş zaman dilimlerinin % 4.91’ini yanlış sezim olarak değerlendirerek toplam 1,125 yürütüm algılamaktadır. Doğruluk, sadece egzersiz sınıflandırması ele alındığında ˘ % 93.46, hem egzersiz hem de yürütüm türü sınıflandırması içinse % 88.65’tir. Sistemin bilinmeyen egzersizlere karşı davranışını sınamak için, algoritma, her egzersiz için, o egzersizin şablonları dışarıda bırakılarak çalıştırılmış ve 1,200 egzersizin sadece 10’u yanlış sezilmiştir. Bu sonuç, sistemin bilinmeyen hareketlere karşı gürbüz olduğunu göstermektedir. Öne sürülen sistem, hem bir fizik tedavi seansının yoğunluğunu kestirmek, hem de hastaya ve fizik tedavi uzmanına geribildirim vermek amacıyla egzersiz hareketlerini değerlendirmek için kullanılabilir.Item Open Access Inertial navigation systems for mobile robots(Institute of Electrical and Electronics Engineers, 1995-06) Barshan, B.; Durrant-Whyte, H. F.A low-cost solid-state inertial navigation system (INS) for mobile robotics applications is described. Error models for the inertial sensors are generated and included in an Extended Kalman Filter (EKF) for estimating the position and orientation of a moving robot vehicle. Two Merent solid-state gyroscopes have been evaluated for estimating the orientation of the robot. Performance of the gyroscopes with error models is compared to the performance when the error models are excluded from the system. The results demonstrate that without error compensation, the error in orientation is between 5-15"/min but can be improved at least by a factor of 5 if an adequate error model is supplied. Siar error models have been developed for each axis of a solid-state triaxial accelerometer and for a conducting-bubble tilt sensor which may also be used as a low-cost accelerometer. Linear position estimation with information from accelerometers and tilt sensors is more susceptible to errors due to the double integration process involved in estimating position. With the system described here, the position drift rate is 1-8 cds, depending on the frequency of acceleration changes. An integrated inertial platform consisting of three gyroscopes, a triaxial accelerometer and two tilt sensors is described. Results from tests of this platform on a large outdoor mobile robot system are described and compared to the results obtained from the robot's own radar-based guidance system. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, low-cost inertial sensing systems can provide valuable orientation and position information particularly for outdoor mobile robot applications.Item Open Access Investigation of sensor placement for accurate fall detection(Springer, 2017) Ntanasis, P.; Pippa, E.; Özdemir, A. T.; Barshan, Billur; Megalooikonomou, V.Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.Item Open Access Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals(2011) Ayrulu-Erdem, B.; Barshan, B.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. © 2011 by the authors; licensee MDPI, Basel, Switzerland.Item Open Access Modeling and simulation of the ferroelectric based micro gyroscope: FEM analysis(Taylor and Francis, 2013-09-23) Ozer, Z.; Mamedov, Amirullah M.; Özbay, EkmelThis paper presents the design and modeling of micro-electromechanical systems (MEMS) on the ternary ferroelectric compounds (PZT) based by using finite element model (FEM) simulation. The conceptual framework establishes five steps to perform the critical analysis: design a novel structure, define the failure mechanisms under the given conditions, analyze different vibrations, analyze the operation principle, and detect resonance modes. In addition, MEMS failure modes were analyzed under different scenarios and the obtained results discussed. Copyright © Taylor & Francis Group, LLC.Item Open Access Modeling of thermal sensitivity of a fiber optic gyroscope coil with practical quadrupole winding(SPIE, 2017) Ogut, Serdar; Osunluk B.; Özbay, EkmelThermally induced bias error is one of the main performance limits for the fiber optic gyroscopes (FOGs). We reviewed the thermal sensitivity of FOG in detail and created a simulation environment by the Finite Element Method (FEM). Thermal sensitivity analysis is based on Shupe and elastooptic effects. Elastooptical interactions are modeled by using the two different FEM simulations and homogenization-dehomogenization processes. FEM simulations are validated by comparing the results with a laboratory FOG setup. We report the changes in the error characteristics for practical quadruple winding patterns. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.Item Open Access Thermally induced bias errors for a fiber coil with practical quadrupole winding(Institute of Electrical and Electronics Engineers Inc., 2017) Osunluk, Berk; Ogut, Serdar; Özbay, EkmelThis paper presents an advanced thermal modeling of a fiber optic gyroscope (FOG) coil. We extended the current models to practical quadrupole winding. Model covers homogenization/dehomogenization parameters of fiber coil. A simulation environment is created by the Finite Element Method (FEM). Simulation environment is validated by comparing the results with laboratory FOG experiments.