Open Access

ARTICLE

Improving the Ambient Intelligence Living Using Deep Learning Classifier

Yazeed Yasin Ghadi¹, Mouazma Batool², Munkhjargal Gochoo³, Suliman A. Alsuhibany⁴, Tamara al Shloul⁵, Ahmad Jalal², Jeongmin Park^6,*

1 Department of Computer Science and Software Engineering, Al Ain University, Al Ain, 15551, UAE
2 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
3 Department of Computer Science and Software Engieering, United Arab Emirates University, Al Ain, 15551, UAE
4 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
5 Department of Humanities and Social Science, Al Ain University, Al Ain, 15551, UAE
6 Department of Computer Engineering, Tech University of Korea, Gyeonggi-do, 15073, Korea

* Corresponding Author: Jeongmin Park. Email:

Computers, Materials & Continua 2022, 73(1), 1037-1053. https://doi.org/10.32604/cmc.2022.027422

Received 17 January 2022; Accepted 30 March 2022; Issue published 18 May 2022

Abstract

Over the last decade, there is a surge of attention in establishing ambient assisted living (AAL) solutions to assist individuals live independently. With a social and economic perspective, the demographic shift toward an elderly population has brought new challenges to today’s society. AAL can offer a variety of solutions for increasing people’s quality of life, allowing them to live healthier and more independently for longer. In this paper, we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks (BiLSTM) and convolutional neural network (CNN) classifier. We first pre-processed the signal data, then used time-frequency features such as signal energy, signal variance, signal frequency, empirical mode, and empirical mode decomposition. The convolutional neural network-bidirectional long-term and short-term memory (CNN-biLSTM) classifier with dimensional reduction isomap algorithm was then used to select ideal features. We assessed the performance of our proposed system on the publicly accessible human gait database (HuGaDB) benchmark dataset and achieved an accuracy rates of 93.95 percent, respectively. Experiments reveal that hybrid method gives more accuracy than single classifier in AAL model. The suggested system can assists persons with impairments, assisting carers and medical personnel.

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Keywords

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