A Mobile Based Activity Recog-nition Model


In the last decade, activity recognition (AR) of humans via smart phones became important and attractive subject for scholars and developers in many areas from health care to real-time security systems. In this research, we worked on AR that based on data col-lected from Android-based smart phone's accelerometers held at waist region while performing different activities (i.e. walking, jogging, climbing stairs, downing stairs, sitting, and standing). To achieve this goal, six classification algorithms were performed: Naïve Bayes (NB), Multi Layer Perceptron (MLP), Bayes Network (BN), Sequential Min-imal Optimization (SMO), Kstar, and Decision Tree (DT). Experi-mental results of the six models were illustrated and analyzed. Com-parison results declare that MLP algorithm outperforms other algo-rithms.