Browsing by Subject "Inertial sensor"

Now showing 1 - 6 of 6

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.
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.
Open Access
Fizik tedavi egzersizlerinin giyilebilir hareket algılayıcıları işaretlerinden dinamik zaman bükmesiyle sezimi ve değerlendirilmesi
(IEEE, 2014-04) Yurtman, Aras; Barshan, Billur
Giyilebilir 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.
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
Investigation of personal variations in activity recognition using miniature inertial sensors and magnetometers
(IEEE, 2012-04) Yurtman, Aras; Barshan, Billur
In this paper, data acquired from five sensory units mounted on the human body, each containing a tri-axial accelerometer, gyroscope, and magnetometer, during 19 different human activities is used to calculate inter-subject and inter-activity variations using different methods and the results are summarized in various forms. Absolute, Euclidean, and dynamic time-warping distances are used to assess the similarity of the signals. The comparisons are made using the raw and normalized time-domain data, raw and normalized feature vectors. Firstly, inter-subject distances are averaged out per activity and per subject. Based on these values, the "best" subject is defined and identified according to his/her average distance to the others. Then, the averages and standard deviations of inter-activity distances are presented per subject, per unit, and per sensor. Moreover, the effects of removing the mean and the different distance measures on the results are discussed. © 2012 IEEE.
Open Access
Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units
(Oxford University Press, 2014-11) Barshan, B.; Yüksek, M. C.
This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered. A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times. © 2013 © The British Computer Society 2013. All rights reserved.