MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning
Abstract This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01860-z |
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| author | Krishna Prakash Musam Naga Harshitha Golla Naga Lakshmi Pallem Moses Madala Sumanth Chowdary Shonak Bansal Mohammad Rashed Iqbal Faruque K. S. Al-Mugren |
| author_facet | Krishna Prakash Musam Naga Harshitha Golla Naga Lakshmi Pallem Moses Madala Sumanth Chowdary Shonak Bansal Mohammad Rashed Iqbal Faruque K. S. Al-Mugren |
| author_sort | Krishna Prakash |
| collection | DOAJ |
| description | Abstract This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular, it is especially helpful for managing cardiovascular disease, tracking stress, improving athletic performance, and providing health care. The device was tested with several machine learning models, such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and Stochastic Gradient Descent (SGD) to identify irregular heart rhythms. Using the SVM model, the system detects problems with an average accuracy of 98%. In the future, MyWear—designed as a wearable T-shirt—will seamlessly integrate with mobile applications for real-time data visualization, enhancing patient outcomes and fostering greater user engagement. |
| format | Article |
| id | doaj-art-40bbb70da6644880a7548f8aa5b8ff6f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-40bbb70da6644880a7548f8aa5b8ff6f2025-08-20T02:32:01ZengNature PortfolioScientific Reports2045-23222025-05-011511810.1038/s41598-025-01860-zMyWear revolutionizes real-time health monitoring with comparative analysis of machine learningKrishna Prakash0Musam Naga Harshitha1Golla Naga Lakshmi2Pallem Moses3Madala Sumanth Chowdary4Shonak Bansal5Mohammad Rashed Iqbal Faruque6K. S. Al-Mugren7Department of Electronics and Communication Engineering, NRI Institute of Technology, AgiripalliDepartment of CSE (AIML), NRI Institute of Technology, AgiripalliDepartment of CSE (AIML), NRI Institute of Technology, AgiripalliDepartment of CSE (AIML), NRI Institute of Technology, AgiripalliDepartment of CSE (AIML), NRI Institute of Technology, AgiripalliDepartment of Electronics and Communication Engineering, Chandigarh UniversitySpace Science Centre (ANGKASA), Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia, UKMPhysics Department, Science College, Princess Nourah Bint Abdulrahman UniversityAbstract This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular, it is especially helpful for managing cardiovascular disease, tracking stress, improving athletic performance, and providing health care. The device was tested with several machine learning models, such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and Stochastic Gradient Descent (SGD) to identify irregular heart rhythms. Using the SVM model, the system detects problems with an average accuracy of 98%. In the future, MyWear—designed as a wearable T-shirt—will seamlessly integrate with mobile applications for real-time data visualization, enhancing patient outcomes and fostering greater user engagement.https://doi.org/10.1038/s41598-025-01860-zHeart rate variability (HRV)Stress level detectionMachine learningK-nearest neighbour (KNN)Support vector machine (SVM) |
| spellingShingle | Krishna Prakash Musam Naga Harshitha Golla Naga Lakshmi Pallem Moses Madala Sumanth Chowdary Shonak Bansal Mohammad Rashed Iqbal Faruque K. S. Al-Mugren MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning Scientific Reports Heart rate variability (HRV) Stress level detection Machine learning K-nearest neighbour (KNN) Support vector machine (SVM) |
| title | MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning |
| title_full | MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning |
| title_fullStr | MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning |
| title_full_unstemmed | MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning |
| title_short | MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning |
| title_sort | mywear revolutionizes real time health monitoring with comparative analysis of machine learning |
| topic | Heart rate variability (HRV) Stress level detection Machine learning K-nearest neighbour (KNN) Support vector machine (SVM) |
| url | https://doi.org/10.1038/s41598-025-01860-z |
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