Wearable sensors-based assistive technologies for patient health monitoring
Introduction:With the advancement of handheld devices, patient health monitoring using wearable devices plays a vital role in overall health monitoring.Methods:In this article, we have integrated multi-model bio-signals to monitor patient health data during daily life activities continuously. Two we...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1437877/full |
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| Summary: | Introduction:With the advancement of handheld devices, patient health monitoring using wearable devices plays a vital role in overall health monitoring.Methods:In this article, we have integrated multi-model bio-signals to monitor patient health data during daily life activities continuously. Two well-known datasets from ScientISST MOVE and mHealth have been analyzed. The purpose of this study is to explore the possibilities of using advanced bio-signals for monitoring patient vital signs during daily life activities and predicting favorable and more accurate health-related solutions based on current body health-related real-time measurements.ResultsWith the help of machine learning algorithms, we have observed classification accuracy of up to 94.67% using the mHealth dataset and 95.12% on the ScientISST MOVE dataset. Other performance indicators, such as recall, precision, and F1 score, also performed well.Discussion:Overall, integrating a machine learning model with bio-signals provides an enhanced ability to interpret complex real-time patient health monitoring for personalized care and overall smart healthcare. |
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| ISSN: | 2296-4185 |