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MedFit: a mobile application for recovering CVD patients

Kuklyte, Jogile, Gualano, Leonardo, Prabhu, Ghanashyama orcid logoORCID: 0000-0003-2836-9734, Venkataraman, Kaushik, Walsh, Deirdre orcid logoORCID: 0000-0003-4255-299X, Woods, Catherine orcid logoORCID: 0000-0002-0892-6591, Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2017) MedFit: a mobile application for recovering CVD patients. In: MMHealth Workshop 2017, 23-27 Oct 2017, Mount View, CA, USA. ISBN 978-1-4503-5504-9

Abstract
The third phase of the recovery from cardiovascular disease (CVD) is an exercise-based rehabilitation programme. However, adherence to an exercise regime is typically not maintained by the patient for a variety of reasons such as lack of time, financial constraints, etc. In order to facilitate patients to perform their exercises from the comfort of their home and at their own convenience, we have developed a mobile application, termed MedFit. It provides access to a tailored suite of exercises along with easy to understand guidance from audio and video instructions. Two types of wearable sensors are utilized to allow motivational feedback to be provided to the user for self monitoring and to provide near real-time feedback. Fitbit, a commercially available activity and fitness tracker, is used to provide in-depth feedback for self-monitoring over longer periods of time (e.g. day, week, month), whereas the Shimmer wireless sensing platform provides the data for near real-time feedback on the quality of the exercises performed. MedFit is a simple and intuitive mobile application designed to provide the motivation and tools for patients to help ensure faster recovery from the trauma caused by CVD. In this paper we describe the MedFit application as a demo submission to the 2nd MMHealth Workshop at ACM MM 2017.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:Cardiovascular disease; Mobile Application; Wearable Sensors; Activity Recognition; Repetition Counting
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: MMHealth '17- Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care. International Workshop on Multimedia for Personal Health and Health Care, Proceedings . ACM Press. ISBN 978-1-4503-5504-9
Publisher:ACM Press
Official URL:https://doi.org/10.1145/3132635.3132651
Copyright Information:© 2017 Association for Computing Machinery (ACM)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland SFI/12/RC/2289, Acqui Bi
ID Code:21910
Deposited On:31 Oct 2017 12:15 by Ghanashyama Prabhu . Last Modified 05 Jan 2022 16:49
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