Cataloged from PDF version of article.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 preprocessing, 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. (C) 2010 Elsevier Ltd. All rights reserve

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.

Bu çalışmada bir denegin bacagına takılan tek eksenli jiroskop sinyallerinin işlenmesiyle birbirinden farklı sekiz bacak hareketi,örüntü tanıma yöntemleriyle ayırdedilmiştir. Ayırdetme işlemi için en küçük kareler, Bayesçi karar verme, k-en yakın komşuluk, dinamik zaman bükmesi, yapay sinir agları ve destek vektör makinesi yöntemleri kullanılarak bu yöntemlerin başarımları birbirleriyle karşılaştırılmıştır. Yapılan çalışma, ileride daha karmaşık algılayıcılarla gerçekleştirilmesiöngörülen daha kapsamlı hareket ayırdetme çalışmaları için bir hazırlık niteligi taşımaktadır.