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<p>The rise of telemedicine has transformed healthcare by enabling remote monitoring and care, especially for elderly patients and those with conditions like Alzheimer’s and Parkinson’s. More specifically, families are more inclined towards being in the vicinity of those relatives. Fall detection in these patients are crucial for their safety and improve their quality of life. Many remote patient monitoring (RPM) frameworks have been suggested, but in the healthcare domain, patient health data and privacy associated with it are of concern. This paper presents Metasave, an avant-garde framework that combines Internet of Things (IoT), Deep Learning (DL), and blockchain technologies to facilitate secure, real-time health monitoring in home environments. The proposed framework enables remote patient monitoring RPM through blockchain-based data sharing and storage, multimodal deep learning techniques for fall detection and hardware sensors that detect balance and motion of the wearer at all times. Zero Knowledge Proof, Merkle Trees and Web3Auth are vital components that factor in for data integrity and user authentication. The multimodal approach of the system demonstrates remarkable performance, with fall detection models achieving over 87% accuracy and a sensitivity of over 98% in all cases.</p>

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Metasave: A novel blockchain-based remote health monitoring framework with IoT and AI

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • M. Rakhee,
  • M. Sudheep Elayidom,
  • Alosh Denny,
  • Fidha Fathima,
  • Harshed Abdulla,
  • Abhinav C. Vadakkaveettil,
  • Hani Atta

摘要

The rise of telemedicine has transformed healthcare by enabling remote monitoring and care, especially for elderly patients and those with conditions like Alzheimer’s and Parkinson’s. More specifically, families are more inclined towards being in the vicinity of those relatives. Fall detection in these patients are crucial for their safety and improve their quality of life. Many remote patient monitoring (RPM) frameworks have been suggested, but in the healthcare domain, patient health data and privacy associated with it are of concern. This paper presents Metasave, an avant-garde framework that combines Internet of Things (IoT), Deep Learning (DL), and blockchain technologies to facilitate secure, real-time health monitoring in home environments. The proposed framework enables remote patient monitoring RPM through blockchain-based data sharing and storage, multimodal deep learning techniques for fall detection and hardware sensors that detect balance and motion of the wearer at all times. Zero Knowledge Proof, Merkle Trees and Web3Auth are vital components that factor in for data integrity and user authentication. The multimodal approach of the system demonstrates remarkable performance, with fall detection models achieving over 87% accuracy and a sensitivity of over 98% in all cases.