Human Activity Recognition Using LSTM Model

Abstract

Detecting the physical body movements of a human is called Human Activity Recognition . HAR is a time series classifying work, means it can be used in different fields, domains and varieties of application. Sensor data are used. The Movements here are often regular activities like walking, jogging, standing and sitting. In general deep learning architecture manages HAR job with a distinct result. Long Short Term Memory (LSTM) neural architecture which is a deep learning model can be designate as a distinct sort of recurrent neural network model this is able to gaining knowledge of long time dependencies in data. The LSTM neural network model comprises a merger of four layers that interact with each other. We have used Long Short Term Memory neural architecture in UCI HAR dataset to identify the type of activity or movement the person is doing. We saw that after the network completes training, optimization and accuracy is found to be 87% to 90% also a graph and confusion matrix has been used to show the losses, accuracies, training iteration, etc.