التفاصيل البيبلوغرافية
| العنوان: |
Learning to estimate heart rate from accelerometer and user's demographics during physical exercises. |
| المؤلفون: |
Pacheco AGC, Cabello FAC, Rodrigues PG, Miraldo DC, Fioravanti VBO, Lima RG, Pinto PR, Fonoff AMO, Penatti OAB |
| المصدر: |
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2023 Mar 02; Vol. PP. Date of Electronic Publication: 2023 Mar 02. |
| Publication Model: |
Ahead of Print |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101604520 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2168-2208 (Electronic) Linking ISSN: 21682194 NLM ISO Abbreviation: IEEE J Biomed Health Inform Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013- |
| مستخلص: |
Getting prompt insights about health and well-being in a non-invasive way is one of the most popular features available on wearable devices. Among all vital signs available, heart rate (HR) monitoring is one of the most important since other measurements are based on it. Real-time HR estimation in wearables mostly relies on photoplethysmography (PPG), which is a fair technique to handle such a task. However, PPG is vulnerable to motion artifacts (MA). As a consequence, the HR estimated from PPG signals is strongly affected during physical exercises. Different approaches have been proposed to deal with this problem, however, they struggle to handle exercises with strong movements, such as a running session. In this paper, we present a new method for HR estimation in wearables that uses an accelerometer signal and user demographics to support the HR prediction when the PPG signal is affected by motion artifacts. This algorithm requires a tiny memory allocation and allows on-device personalization since the model parameters are finetuned in real time during workout executions. Also, the model may predict HR for a few minutes without using a PPG, which represents a useful contribution to an HR estimation pipeline. We evaluate our model on five different exercise datasets - performed on treadmills and in outdoor environments - and the results show that our method can improve the coverage of a PPG-based HR estimator while keeping a similar error performance, which is particularly useful to improve user experience. |
| تواريخ الأحداث: |
Date Created: 20230407 Latest Revision: 20240215 |
| رمز التحديث: |
20240215 |
| DOI: |
10.1109/JBHI.2023.3251742 |
| PMID: |
37028018 |
| قاعدة البيانات: |
MEDLINE |
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