Sensor based Human Activity Recognition

Abstract

Human activity recognition (HAR) is to recognize another person’s activities and it is one of the active research areas in the computer field. The goal of this System is to understand people's actions and interactions. We proposed a method of Human Activity is by predicting the person's activity, their personality, and their psychological state like Human activity recognition (HAR). We propose a recurrent neural network of deep learning architecture. The critical factor of RNN includes bidirectional connection that is simply called from the input node, the information only flows in forwarding direction after that it passthrough so many hidden layers to reach the output.. This system is to design the six different activities of a human. The final model should use as a good source of information about human's daily activities. The dataset has taken from UCI Machine Learning Repository. Our system accuracy is higher than the previous results.