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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1437877/full |
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| author | Nouf Abdullah Almujally Danyal Khan Danyal Khan Naif Al Mudawi Mohammed Alonazi Haifa F. Alhasson Ahmad Jalal Ahmad Jalal Hui Liu Hui Liu Hui Liu |
| author_facet | Nouf Abdullah Almujally Danyal Khan Danyal Khan Naif Al Mudawi Mohammed Alonazi Haifa F. Alhasson Ahmad Jalal Ahmad Jalal Hui Liu Hui Liu Hui Liu |
| author_sort | Nouf Abdullah Almujally |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3f6ecaea53ac4b32a55eb372c661389f |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-3f6ecaea53ac4b32a55eb372c661389f2025-08-20T03:36:34ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-06-011310.3389/fbioe.2025.14378771437877Wearable sensors-based assistive technologies for patient health monitoringNouf Abdullah Almujally0Danyal Khan1Danyal Khan2Naif Al Mudawi3Mohammed Alonazi4Haifa F. Alhasson5Ahmad Jalal6Ahmad Jalal7Hui Liu8Hui Liu9Hui Liu10Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaFaculty of Computer Science and AI, Air University, Islamabad, PakistanDepartment of Computer Science, National University of Modern Languages NUML, Islamabad, PakistanDepartment of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi ArabiaDepartment of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaFaculty of Computer Science and AI, Air University, Islamabad, PakistanDepartment of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of KoreaGuodian Nanjing Automation Co., Ltd., Nanjing, ChinaJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, China0Cognitive Systems Lab, University of Bremen, Bremen, GermanyIntroduction: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.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1437877/fullpatient monitoringwearable sensorsaccelerometersbiosensorshealthcarehuman-machine interaction |
| spellingShingle | Nouf Abdullah Almujally Danyal Khan Danyal Khan Naif Al Mudawi Mohammed Alonazi Haifa F. Alhasson Ahmad Jalal Ahmad Jalal Hui Liu Hui Liu Hui Liu Wearable sensors-based assistive technologies for patient health monitoring Frontiers in Bioengineering and Biotechnology patient monitoring wearable sensors accelerometers biosensors healthcare human-machine interaction |
| title | Wearable sensors-based assistive technologies for patient health monitoring |
| title_full | Wearable sensors-based assistive technologies for patient health monitoring |
| title_fullStr | Wearable sensors-based assistive technologies for patient health monitoring |
| title_full_unstemmed | Wearable sensors-based assistive technologies for patient health monitoring |
| title_short | Wearable sensors-based assistive technologies for patient health monitoring |
| title_sort | wearable sensors based assistive technologies for patient health monitoring |
| topic | patient monitoring wearable sensors accelerometers biosensors healthcare human-machine interaction |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1437877/full |
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