DEEP LEARNING FRAMEWORK FOR HUMAN ACTIVITY DETECTION BASED ON WIRELESS SENSORS.

Abstract

Recognizing human athletic behaviors has grown in importance as a component of analysis during the last few years. For upcoming training, FPGA-based recognition has been built to perfectly predict human actions. Intelligent environments where human athletic movements may be automatically recognized are required to ensure the viability of such long-term athletic training monitoring systems. The system discussed here employs an Artificial Neural Network (ANN) based Field-Programmable Gate Array (FPGA). In order to infer human behavior from the data collected by a smart environment, machine learning algorithms must first be trained on annotated datasets. To effectively extract characteristics from unprocessed data and develop a machine learning model for anticipating an individual's movement, conventional signal processing techniques and domain knowledge are required. The purpose of this work is to show how a hybrid deep learning model may be applied to recognize human behavior. Convolutional neural networks and continuous neural networks, for example, are deep learning techniques that will extract the features and help with categorization. The proposed model forecasts human activity using wireless sensor data mining datasets. Accuracy, training loss, testing loss, the confusion matrix, and other metrics have all been used to evaluate the model's performance.