⚡ Quick Summary
This study introduces a novel metaheuristic optimization-driven ensemble model for smart monitoring of indoor activities, specifically designed for individuals with disabilities. The proposed MOEM-SMIADP model achieved an impressive accuracy of 99.07%, showcasing the potential of AI and IoT technologies in enhancing the quality of life for disabled persons.
🔍 Key Details
- 📊 Dataset: Indoor activity data for individuals with disabilities
- ⚙️ Technology: Ensemble of Graph Convolutional Network, LSTM-seq2seq, and Convolutional Autoencoder
- 🧩 Feature Selection: Marine Predator Algorithm
- 🏆 Performance: Accuracy of 99.07% using the MOEM-SMIADP model
🔑 Key Takeaways
- 🌐 IoT integration provides advanced solutions for monitoring indoor activities.
- 🤖 AI-driven models can significantly enhance the detection and classification of human activities.
- 📈 Ensemble learning techniques improve accuracy in activity recognition.
- 🔍 Feature selection is crucial for optimizing model performance.
- 💡 The study highlights the transformative potential of technology for nearly one billion disabled individuals worldwide.
- 🏥 Practical applications include improved healthcare and support services for disabled persons.
- 📅 Published in Scientific Reports, 2025.
- 🆔 PMID: 39910242.
📚 Background
The demand for healthcare services among disabled individuals is a growing global concern. Traditional support systems often involve high costs and complex care requirements. However, the Internet of Things (IoT) offers innovative solutions that can enhance the quality of life for disabled persons by providing effective monitoring and support through smart technologies.
🗒️ Study
This research focuses on developing the MOEM-SMIADP model, which utilizes an ensemble of advanced machine learning techniques to monitor indoor activities of individuals with disabilities. The study emphasizes the importance of data preprocessing and feature selection to ensure the model’s effectiveness in real-world applications.
📈 Results
The performance validation of the MOEM-SMIADP model demonstrated a remarkable accuracy of 99.07%, significantly outperforming existing methods. This high level of accuracy indicates the model’s potential for reliable activity detection and classification, which is essential for providing timely support to disabled individuals.
🌍 Impact and Implications
The findings of this study could have profound implications for the healthcare sector, particularly in enhancing the support services available to disabled persons. By leveraging AI and IoT technologies, we can create more inclusive environments that promote independence and improve overall quality of life for individuals with disabilities.
🔮 Conclusion
This research highlights the significant potential of artificial intelligence in transforming the monitoring of indoor activities for disabled individuals. The successful implementation of the MOEM-SMIADP model paves the way for future advancements in healthcare technology, encouraging further exploration and development in this promising field.
💬 Your comments
What are your thoughts on the integration of AI and IoT in supporting individuals with disabilities? We would love to hear your insights! 💬 Leave your comments below or connect with us on social media:
Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities.
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.
Author: [‘Arasi MA’, ‘AlEisa HN’, ‘Alneil AA’, ‘Marzouk R’]
Journal: Sci Rep
Citation: Arasi MA, et al. Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities. Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities. 2025; 15:4337. doi: 10.1038/s41598-025-88450-1