Comput Math Methods Med - SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

Tópicos

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Resumo

Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

Resumo Limpo

two approach classif differ locomotor activ perform various speed present evalu maximum posteriori map bay classif scheme support vector machin svm appli d project featur extract acceleromet data locomotor activ level walk stair climb stair descend record inerti sensor place shank prefer leg perform natur indooroutdoor scenario healthi young adult age yrs segment activ epoch sixteen featur chosen frequenc time domain dimens reduct perform d sammon map artifici neural network ann train mimic sammon map whole dataset bay approach two featur fed bay classifi incorpor updat rule svm scheme ann consid kernel function classifi bay approach perform slight better svm train set versus test set versus favor propos bay scheme suitabl propos svm distinguish among differ monitor activ

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