IoT-assisted Human Activity Recognition Using Bat Optimization Algorithm with Ensemble Voting Classifier for Disabled Persons

Abstract

Internet of Things (IoT)-based human action recognition (HAR) has made a significant contribution to scientific studies. Furthermore, hand gesture recognition is a subsection of HAR, and plays a vital role in interacting with deaf people. It is the automatic detection of the actions of one or many subjects using a series of observations. Convolutional neural network structures are often utilized for finding human activities. With this intention, this study presents a new bat optimization algorithm with an ensemble voting classifier for human activity recognition (BOA-EVCHAR) technique to help disabled persons in the IoT environment. The BOA-EVCHAR technique makes use of the ensemble classification concept to recognize human activities proficiently in the IoT environment. In the presented BOA-EVCHAR approach, data preprocessing is generally achieved at the beginning level. For the identification and classification of human activities, an ensemble of two classifiers namely long short-term memory (LSTM) and deep belief network (DBN) models is utilized. Finally, the BOA is used to optimally select the hyperparameter values of the LSTM and DBN models. To elicit the enhanced performances of the BOA-EVCHAR technique, a series of experimentation analyses were performed. The extensive results of the BOA-EVCHAR technique show a superior value of 99.31% on the HAR process.