Internet of Medical Things

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

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EWPS: Emergency data communication in the Internet of Medical Things

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3053419 ◽

2021 ◽

pp. 1-1

Author(s):

S. Gopikrishnan ◽

P. Priakanth ◽

Gautam Srivastava ◽

Giancarlo Fortino

Keyword(s):

Data Communication ◽

The Internet ◽

Internet Of Medical Things

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On the internet of medical things

Communications of the ACM ◽

10.1145/3406779 ◽

2020 ◽

Vol 63 (8) ◽

pp. 5-5

Author(s):

Vinton G. Cerf

Keyword(s):

The Internet ◽

Internet Of Medical Things

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Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3088465 ◽

2021 ◽

pp. 1-1

Author(s):

Junxin Chen ◽

Shuang Sun ◽

Li-bo Zhang ◽

Benqiang Yang ◽

Wei Wang

Keyword(s):

Compressed Sensing ◽

Heart Sound ◽

Internet Of Medical Things

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An analysis of the current status of Internet of Medical Things policies in developing countries

NPG Neurologie - Psychiatrie - Gériatrie ◽

10.1016/j.npg.2020.03.002 ◽

2020 ◽

Vol 20 (119) ◽

pp. 259-265 ◽

Cited By ~ 1

Author(s):

M. Kante ◽

P. Ndayizigamiye

Keyword(s):

Developing Countries ◽

Current Status ◽

Internet Of Medical Things

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Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments

Sensors ◽

10.3390/s21103474 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3474

Author(s):

Taehoon Kim ◽

Wonbin Kim ◽

Daehee Seo ◽

Imyeong Lee

Keyword(s):

Internet Of Things ◽

Diagnosis And Treatment ◽

Key Recovery ◽

Collusion Attacks ◽

Malicious User ◽

Systems Security ◽

Recovery System ◽

Multi Agent ◽

Internet Of Medical Things ◽

Gain Access

Recently, as Internet of Things systems have been introduced to facilitate diagnosis and treatment in healthcare and medical environments, there are many issues concerning threats to these systems’ security. For instance, if a key used for encryption is lost or corrupted, then ciphertexts produced with this key cannot be decrypted any more. Hence, this paper presents two schemes for key recovery systems that can recover the lost or the corrupted keys of an Internet of Medical Things. In our proposal, when the key used for the ciphertext is needed, this key is obtained from a Key Recovery Field present in the cyphertext. Thus, the recovered key will allow decrypting the ciphertext. However, there are threats to this proposal, including the case of the Key Recovery Field being forged or altered by a malicious user and the possibility of collusion among participating entities (Medical Institution, Key Recovery Auditor, and Key Recovery Center) which can interpret the Key Recovery Field and abuse their authority to gain access to the data. To prevent these threats, two schemes are proposed. The first one enhances the security of a multi-agent key recovery system by providing the Key Recovery Field with efficient integrity and non-repudiation functions, and the second one provides a proxy re-encryption function resistant to collusion attacks against the key recovery system.

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