Blockchain and DL-based Communicable Disease Spread Control Framework by Monitoring Quarantine Activities
In the face of global health crises, ensuring compliance with quarantine regulations is paramount for effectively controlling the spread of infectious diseases. This paper presents a novel system for monitoring activities among quarantined patients using wearable devices, artificial intelligence (AI) models, and blockchain technology. Our system enables continuous monitoring of patients’ activities and provides real-time predictions of their compliance status while securely storing and utilizing data on a blockchain. By leveraging wearable devices and employing AI techniques, our system offers an automated and efficient solution for quarantine enforcement. The system’s effectiveness is demonstrated through its ability to accurately predict compliance and trigger necessary interventions when deviations from designated areas occur. Our approach is able to achieve a 98.92% accuracy score to predict the activity of the subject. We also demonstrate the effect of different optimizers and their performance. The proposed approach, enhanced by blockchain technology, holds promise for reducing manual efforts, enhancing healthcare workers’ safety, and ensuring the integrity and privacy of patient data during the monitoring process.
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Blockchain and DL-based Communicable Disease Spread Control Framework by Monitoring Quarantine Activities
Semantic Scholar · 2023
Abstract
In the face of global health crises, ensuring compliance with quarantine regulations is paramount for effectively controlling the spread of infectious diseases. This paper presents a novel system for monitoring activities among quarantined patients using wearable devices, artificial intelligence (AI) models, and blockchain technology. Our system enables continuous monitoring of patients’ activities and provides real-time predictions of their compliance status while securely storing and utilizing data on a blockchain. By leveraging wearable devices and employing AI techniques, our system offers an automated and efficient solution for quarantine enforcement. The system’s effectiveness is demonstrated through its ability to accurately predict compliance and trigger necessary interventions when deviations from designated areas occur. Our approach is able to achieve a 98.92% accuracy score to predict the activity of the subject. We also demonstrate the effect of different optimizers and their performance. The proposed approach, enhanced by blockchain technology, holds promise for reducing manual efforts, enhancing healthcare workers’ safety, and ensuring the integrity and privacy of patient data during the monitoring process.