A4 Refereed article in a conference publication
Optimising Personalised Medical Insights by Introducing a Scalable Health Informatics Application for Sensor Data Extraction, Preprocessing, and Analysis
Authors: Hettiarachchi, Chirath; Vlieger, Robin; Ge, Wenbo; Apthorp, Deborah; Daskalaki, Elena; Brüstle, Anne; Suominen, Hanna
Editors: Bichel-Findlay, Jen
Conference name: Australian Digital Health and Health Informatics Conference
Publication year: 2024
Journal: Studies in Health Technology and Informatics
Book title : Health. Innovation. Community: It Starts With Us: Papers from the 28th Australian Digital Health and Health Informatics Conference (HIC 2024), Brisbane, Australia, 5–7 August 2024
Journal name in source: Studies in health technology and informatics
Journal acronym: Stud Health Technol Inform
Volume: 318
First page : 138
Last page: 143
eISBN: 978-1-64368-541-0
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI240905
Web address : https://ebooks.iospress.nl/doi/10.3233/SHTI240905
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/458309891
Wearable sensors, among other informatics solutions, are readily accessible to enable noninvasive remote monitoring in healthcare. While providing a wealth of data, the wide variety of such sensing systems and the differing implementations of the same or similar sensors by different developers complicate comparisons of collected data. An online application as a platform technology that provides uniform methods for analysing balance data is presented as a case study. The development of balance problems is common in neurodegenerative conditions, leading to falls and a reduced quality of life. While balance can be assessed using, for example, perturbation tests, sensors offer a more quantitative and scalable way. Researchers can adjust the platform to integrate the sensors of their choice or upload data and then preprocess, featurise, analyse, and visualise them. This eases performing comparative analyses across the sensors and datasets through a reduction of heterogeneity and facilitates easy integration of machine learning and other advanced data analytics, thereby targeting personalising medical insights.
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Funding information in the publication:
This research was funded by and has been delivered in partnership with Our Health in Our Hands (OHIOH), a strategic initiative of the Australian National University (ANU), which aims to transform healthcare by developing new personalised health technologies and solutions in collaboration with patients, clinicians, and healthcare providers. We gratefully acknowledge the funding from the ANU School of Computing and the Australian Government Research Training Program (AGRTP) for the first three authors’ PhD studies
