With the aid of advancing technologies, the healthcare sector is improving more swiftly than ever, yet there are still many unexplored areas and there is always potential for improvement. Deep Learning (DL), blockchain and the Internet of Medical Things (IoMT), three of the most revolutionary technologies, have aided replace conventional practice employed in the healthcare industry with more sophisticated and efficient procedures. In this research, we propose DON-B-STRESSED (DBS), an innovative framework which integrates DL, blockchain and IoMT technologies to provide a method for the early detection of stress in users donning IoMT devices. Long-term stress, indeed, is well-known to have profound consequences on human wellbeing; hence the need for automated solutions for continual stress monitoring. DON-B-STRESSED stands for DON a Blockchain-based, Secure, Timely, REliable System for StrEss Detection: the premise behind this is that if the above-mentioned technologies can be combined, a system that employs IoMT for users' data collection, DL to properly predict patients' stress levels, and smart contracts deployed on blockchain to ensure secure transactions and health checks, the well-known concerns of harmful effects of long-term stress might be avoided. By using multimodal data from both a chest- and a wrist-worn IoMT device, we have developed a tailored, secure, accurate yet fast real-time system for the early detection of stress signs. The DL part has been evaluated on WESAD, a publicly available dataset for wearable stress and affect detection, reaching an accuracy of more than 99% with LOSO generalization scheme. Furthermore, the scalability and reliability of DON-B-STRESSED are shown by comparison with the findings of past research.

A blockchain-based secure Internet of medical things framework for stress detection / Pian, Qi; Chiaro, D.; Giampaolo, F.; Piccialli, F.. - In: INFORMATION SCIENCES. - ISSN 0020-0255. - 628:(2023), pp. 377-390. [10.1016/j.ins.2023.01.123]

A blockchain-based secure Internet of medical things framework for stress detection

Chiaro D.
;
Giampaolo F.;Piccialli F.
2023

Abstract

With the aid of advancing technologies, the healthcare sector is improving more swiftly than ever, yet there are still many unexplored areas and there is always potential for improvement. Deep Learning (DL), blockchain and the Internet of Medical Things (IoMT), three of the most revolutionary technologies, have aided replace conventional practice employed in the healthcare industry with more sophisticated and efficient procedures. In this research, we propose DON-B-STRESSED (DBS), an innovative framework which integrates DL, blockchain and IoMT technologies to provide a method for the early detection of stress in users donning IoMT devices. Long-term stress, indeed, is well-known to have profound consequences on human wellbeing; hence the need for automated solutions for continual stress monitoring. DON-B-STRESSED stands for DON a Blockchain-based, Secure, Timely, REliable System for StrEss Detection: the premise behind this is that if the above-mentioned technologies can be combined, a system that employs IoMT for users' data collection, DL to properly predict patients' stress levels, and smart contracts deployed on blockchain to ensure secure transactions and health checks, the well-known concerns of harmful effects of long-term stress might be avoided. By using multimodal data from both a chest- and a wrist-worn IoMT device, we have developed a tailored, secure, accurate yet fast real-time system for the early detection of stress signs. The DL part has been evaluated on WESAD, a publicly available dataset for wearable stress and affect detection, reaching an accuracy of more than 99% with LOSO generalization scheme. Furthermore, the scalability and reliability of DON-B-STRESSED are shown by comparison with the findings of past research.
2023
A blockchain-based secure Internet of medical things framework for stress detection / Pian, Qi; Chiaro, D.; Giampaolo, F.; Piccialli, F.. - In: INFORMATION SCIENCES. - ISSN 0020-0255. - 628:(2023), pp. 377-390. [10.1016/j.ins.2023.01.123]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/914741
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