TY  - EJOU
AU  - Ghadi, Yazeed Yasin 
AU  - Batool, Mouazma 
AU  - Gochoo, Munkhjargal 
AU  - Alsuhibany, Suliman A. 
AU  - Shloul, Tamara al 
AU  - Jalal, Ahmad 
AU  - Park, Jeongmin 

TI  - Improving the Ambient Intelligence Living Using Deep Learning Classifier
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 73
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Ambient assisted living; convolutional neural network; dimensionality reduction; frequency-time features; wearable technology

DO  - 10.32604/cmc.2022.027422