Smartphone Sensor Based Human Activity Recognition using Deep Learning Models

Abstract

The significant intent is to generate the model for anticipating the activities of a human that ensures the aversion of human life. Activity Recognition (AR) is monitoring the liveliness of a person by using smart phone. Smart phones are used in a wider manner and it becomes one of the ways to identify the human’s environmental changes by using the sensors in smart mobiles. Smart phones are equipped in detecting sensors like compass sensor, gyroscope, GPS sensor and accelerometer. The contraption is demonstrated to examine the state of an individual. Human Activity Recognition (HAR) framework collects the raw data from sensors and observes the human movement using different deep learning approach. Deep learning models are proposed to identify motions of humans with plausible high accuracy by using sensed data. HAR Dataset from UCI dataset storehouse is utilized. The performance of a framework is analyzed using Convolutional Neural Network with Long-Short Term Memory [ConvLSTM] and Recurrent Neural Network with Long-Short Term Memory [RNNLSTM] using only the raw data. The act of the model is analyzed in terms of exactness and efficiency. The designed activity recognition model can be manipulated in medical domain for predicting any disease by monitoring human actions.