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Implementation of Mobile-Based Real-Time Heart Rate Variability Detection for Personalized Healthcare
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: 2019 International Conference on Data Mining Workshops (ICDMW): Proceedings / [ed] Panagiotis Papapetrou, Xueqi Cheng, Qing He, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The ubiquity of wearable devices together with areas like internet of things, big data and machine learning have promoted the development of solutions for personalized healthcare that use digital sensors. However, there is a lack of an implemented framework that is technically feasible, easily scalable and that provides meaningful variables to be used in applications for translational medicine. This paper describes the implementation and early evaluation of a physiological sensing tool that collects and processes photoplethysmography data from a wearable smartwatch to calculate heart rate variability in real-time. A technical open-source framework is outlined, involving mobile devices for collection of heart rate data, feature extraction and execution of data mining or machine learning algorithms that ultimately deliver mobile health interventions tailored to the users. Eleven volunteers participated in the empirical evaluation that was carried out using an existing mobile virtual reality application for mental health and under controlled slow-paced breathing exercises. The results validated the feasibility of implementation of the proposed framework in the stages of signal acquisition and real-time calculation of heart rate variability (HRV). The analysis of data regarding packet loss, peak detection and overall system performance provided considerations to enhance the real-time calculation of HRV features. Further studies are planned to validate all the stages of the proposed framework.

Place, publisher, year, edition, pages
IEEE, 2019.
Series
IEEE International Conference on Data Mining workshops, ISSN 2375-9232, E-ISSN 2375-9259
Keywords [en]
Heart rate variability, HRV, personalized health, framework, mobile, real-time, smartwatch
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-178338DOI: 10.1109/ICDMW.2019.00123OAI: oai:DiVA.org:su-178338DiVA, id: diva2:1388406
Conference
19th IEEE International Conference on Data Mining Workshops (ICDMW), Beijing, China, 8–11 November, 2019
Available from: 2020-01-24 Created: 2020-01-24 Last updated: 2020-01-25Bibliographically approved

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