Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Abstract

Human activity recognition is a time series classification problem that is difficult to solve (HAR). Traditional signal processing approaches and domain expertise are necessary to appropriately create features from raw data and fit a machine learning model for predicting a person's movement. This work aims to demonstrate how a hybrid deep learning model may be used to recognize human behavior. Deep learning methodologies such as convolutional neural networks and recurrent neural networks will extract the features and achieve the classification goal. The suggested model has used wireless sensor data mining datasets to predict human activity. The model's performance has been assessed using the confusion matrix, accuracy, training loss, and testing loss. Thus, the model has achieved greater than 96% accuracy, superior to other state-of-the-art algorithms in this field.