Smartwatch-Based Data Analytics and Feature Selection for Heart Failure Assessment

Xu Jun Jian, Shiyang Lyu, Chao Hung Wang, David Taniar*, Tieh Cheng Fu, Tun Wen Pai

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

Abstract

Patients with heart failure require long-term or frequent hospitalization, which places a heavy burden on medical resources. The six-minute walk test is a simple and cost-effective method for assessing aerobic capacity and endurance. It does not require specialized personnel or sophisticated equipment and involves recording walking distance, blood pressure, heart rate, and oxygen saturation level within a fixed time interval. In this study, we provided patients with heart failure with a smart watch and an application tool, enabling them to perform the six-minute walk test at home. The application allowed patients to upload their test data on cloud storage, which were examined using feature correlation analysis, regression modeling, and other techniques. The goal was to explore the most influential features that correlated with outpatient records and provide effective reminders to patients with heart failure to monitor their health status during their daily lives, which would reduce medical resource consumption.

Original languageEnglish
JournalInternational Journal of Mobile Computing and Multimedia Communications
Volume16
Issue number1
DOIs
StatePublished - 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025 IGI Global. All rights reserved.

Keywords

  • Cardiopulmonary Exercise Test (CPET)
  • Heart Failure
  • Machine Learning
  • Six-Minute Walk Test (6MWT)

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