Human activity classification with miniature inertial and magnetic sensors

This study provides a comparative performance assessment of various pattern recognition techniques on classifying human activities that are performed while wearing miniature inertial and magnetic sensors. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. The classification techniques compared in this study are: naive Bayesian (NB), artificial neural networks (ANN), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). According to the outcome of the study, the three methods that result in the highest correct differentiation rates are GMM (99.12%), ANN (99.09%), and SVM (99.80%).