Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities.
Journal:
Scientific reports
PMID:
39910242
Abstract
Disabled persons demanding healthcare is a developing global occurrence. The support in longer-term care includes nursing, intricate medical, recovery, and social help services. The price is large, but advanced technologies can aid in decreasing expenditure by certifying effective health services and enhancing the superiority of life. The transformative latent of the Internet of Things (IoT) prolongs the existence of nearly one billion persons worldwide with disabilities. By incorporating smart devices and technologies, the IoT provides advanced solutions to tackle numerous tasks challenged by individuals with disabilities and promote equality. Human activity detection methods are the technical area which studies the classification of actions or movements an individual achieves over the recognition of signals directed by smartphones or wearable sensors or over images or video frames. They are efficient in certifying functions of detection of actions, observing crucial functions, and tracking. Conventional machine learning and deep learning approaches effectively detect human activity. This study develops and designs a metaheuristic optimization-driven ensemble model for smart monitoring of indoor activities for disabled persons (MOEM-SMIADP) model. The proposed MOEM-SMIADP model concentrates on detecting and classifying indoor activities using IoT applications for physically challenged people. First, data preprocessing is performed using min-max normalization to convert input data into useful format. Furthermore, the marine predator algorithm is employed in feature selection. For the detection of indoor activities, the proposed MOEM-SMIADP model utilizes an ensemble of three classifiers, namely the graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, and convolutional autoencoder. Eventually, the hyperparameter tuning is accomplished by an improved coati optimization algorithm to enhance the classification outcomes of ensemble models. A wide range of experiments was accompanied to endorse the performance of the MOEM-SMIADP technique. The performance validation of the MOEM-SMIADP technique portrayed a superior accracy value of 99.07% over existing methods.