Altun, KeremBarshan, Billur2016-02-082016-02-0820100302-9743http://hdl.handle.net/11693/28535Conference name: First International Workshop, HBU 2010Date of Conference: August 22, 2010This 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.EnglishClassification ratesClassification techniqueComparative studiesComputational costsCross-validation techniqueDynamic time warpingFeature reductionFeature selectionfeature selection and reductionHuman activitiesHuman activity recognitionInertial sensorK-nearest neighbor algorithmLeast squares methodsReal-time applicationSensor unitsSports activityTri-axial magnetometerTriaxial accelerometerBayesian networksBehavioral researchClassifiersDecision makingInertial navigation systemsIntelligent agentsMagnetometersNeural networksPrincipal component analysisSensorsSupport vector machinesFeature extractionHuman activity recognition using inertial/magnetic sensor unitsConference Paper10.1007/978-3-642-14715-9_510.1007/978-3-642-14715-9