Data Quality and the Internet of Medical Things (IoMT)

This paper summarises a programme of PhD research relevant to federated machine learning for IoMT-informed patient care. Sensing performance, data quality and stakeholder perceptions are key to the clinical outcomes that can be achieved in future healthcare systems. However, IoMT data quality challenges are complex and multi-faceted, and could significantly impact clinical decisions that depend on accurate and timely data. The paper outlines the research context and challenges, summarises progress and candidate research questions, and discusses potential solutions, and topics for future research in this area.

Content

Author and article information

Contributors

Arshad Farhad

Sandra Woolley

Conference

Publication date: July 2022

Publication date (Print): July 2022

Pages: 1-2

Affiliations

[0001]School of Computing and Mathematics

Keele University, UK

Article

DOI: 10.14236/ewic/HCI2022.65

SO-VID: 1290df52-16a1-4a88-b608-5efac1659dfa

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 35th International BCS Human-Computer Interaction Conference

Conference acronym: HCI2022

Conference number: 35

Conference location: Keele, Staffordshire

Conference date: July 11th to 13th, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Towards a Human-Centred Digital Society

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.65

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58
Chen, K.-C., & Lien, S.-Y. (2014). Machine-to-machine communications: Technologies and challenges. Ad Hoc Networks, 18, 3-23
Farhad, A, Woolley, S.I, Andras, A, “A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things”, Medical Informatics Europe Conference (MIE), In Public Health and Informatics (pp. 504-505). IOS Press.2021.
Farhad, A., Woolley, S.I., and Andras, P. “Federated learning for AI to improve patient care using wearable and IoMT sensors”, In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 434-434, IEEE.
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016a). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57-81.
Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security: Challenges and 14 Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 11, 100227.
Perez-Castillo, R., Carretero, A. G., Rodriguez, M., Caballero, I., & Piattini, M. (2018). Data Quality Best Practices in IoT Environments. 2018 International Conference on the Quality of Information and Communications Technology, 272-275
Ray, D., Collins, T., Woolley, S. and Ponnapalli, P., 2021. A Review of Wearable Multi-wavelength Photoplethysmography. IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2021.3121476.
Wei, X., Liu, Y., Wang, X., Sun, B., Gao, S., & Rokne, J. (2019a). A survey on quality-assurance approximate stream processing and applications. Future Generation Computer Systems, 1062-1080.

Comments

Comment on this article

[1] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58

[2] Chen, K.-C., & Lien, S.-Y. (2014). Machine-to-machine communications: Technologies and challenges. Ad Hoc Networks, 18, 3-23

[3] Farhad, A, Woolley, S.I, Andras, A, “A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things”, Medical Informatics Europe Conference (MIE), In Public Health and Informatics (pp. 504-505). IOS Press.2021.

[4] Farhad, A., Woolley, S.I., and Andras, P. “Federated learning for AI to improve patient care using wearable and IoMT sensors”, In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 434-434, IEEE.

[5] Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016a). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57-81.

[6] Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security: Challenges and 14 Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 11, 100227.

[7] Perez-Castillo, R., Carretero, A. G., Rodriguez, M., Caballero, I., & Piattini, M. (2018). Data Quality Best Practices in IoT Environments. 2018 International Conference on the Quality of Information and Communications Technology, 272-275

[8] Ray, D., Collins, T., Woolley, S. and Ponnapalli, P., 2021. A Review of Wearable Multi-wavelength Photoplethysmography. IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2021.3121476.

[9] Wei, X., Liu, Y., Wang, X., Sun, B., Gao, S., & Rokne, J. (2019a). A survey on quality-assurance approximate stream processing and applications. Future Generation Computer Systems, 1062-1080.

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