Human activity recognition in IoHT applications using Arithmetic Optimization Algorithm and deep learning. (August 2022)

• Record Type: Journal Article
• Authors: Dahou, Abdelghani; Al-qaness, Mohammed A.A.; Abd Elaziz, Mohamed; Helmi, Ahmed
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Human Activity Recognition (HAR) -- Human–computer interaction (HCI) -- Internet of Healthcare Things (ioHT) -- Feature selection -- Arithmetic Optimization Algorithm; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111445
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Nowadays, people use smart devices everywhere and for different applications such as healthcare. The Internet of Healthcare Things (IoHT) generates enormous amounts of data daily, which need exploitation and analysis to help healthcare professionals make decisions and provide a fast diagnosis. Human Activity Recognition (HAR) has received more attention due to its importance in elderly care, lifestyle improvement, and IoT systems. This paper presents a novel HAR system based on optimizing two algorithms: convolutional neural network (CNN) and the recently proposed optimization algorithm, Arithmetic Optimization Algorithm (AOA), to boost the HAR performance with fewer resources. The proposed CNN is applied to learn and extract features from input data where a modified AOA algorithm, called Binary AOA (BAOA), is used to select the most optimal features. Finally, the support vector machine (SVM) is adopted to classify the selected feature based on different activities. We evaluate the proposed HAR model with three different public datasets, UCI-HAR, WISDM-HAR, and KU-HAR datasets. Moreover, we compare the feature selection method, BAOA, to various optimization algorithms using several evaluation measures, and we found that BAOA recorded the best performance. Furthermore, we compare the proposed model to several existing HAR methods. The outcomes confirmed the competitive performance of the proposed model, which achieved 95.23%, 99.5%, and 96.8% for UCI-HAR, WISDM-HAR, and KU-HAR datasets, respectively. Highlights: Propose a new HAR system using the advantages of Metaheuristic and deep learning. Develop a feature extraction method using deep CNN to expose relevant features. Propose a feature selection method based on Binary Athematic Optimization Algorithm Implement extensive evaluation experiment using three public HAR datasets.