Evidence map›Paper›PMID 40379805›Full record

ArticleScientific reports2025

MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning.

Krishna Prakash, Musam Naga Harshitha, Golla Naga Lakshmi, Pallem Moses, Madala Sumanth Chowdary, Shonak Bansal, Mohammad Rashed Iqbal Faruque, K S Al-Mugren

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Krishna PrakashDepartment of Electronics and Communication Engineering, NRI Institute of Technology, Agiripalli, Eluru, Andhra Pradesh, 521212, India. k_krishna2k7@yahoo.co.in.
Musam Naga HarshithaDepartment of CSE (AIML), NRI Institute of Technology, Agiripalli, Eluru, Andhra Pradesh, 521212, India.
Golla Naga LakshmiDepartment of CSE (AIML), NRI Institute of Technology, Agiripalli, Eluru, Andhra Pradesh, 521212, India.
Pallem MosesDepartment of CSE (AIML), NRI Institute of Technology, Agiripalli, Eluru, Andhra Pradesh, 521212, India.
Madala Sumanth ChowdaryDepartment of CSE (AIML), NRI Institute of Technology, Agiripalli, Eluru, Andhra Pradesh, 521212, India.
Shonak BansalDepartment of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab, India. shonakk@gmail.com.
Mohammad Rashed Iqbal FaruqueSpace Science Centre (ANGKASA), Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor D. E., Malaysia. rashed@ukm.edu.my.
K S Al-MugrenPhysics Department, Science College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

Ministry of Education (MOE), MY FRGS/1/2022/TK07/UKM/02/22Princess Nourah Bint Abdulrahman University PNURSP2025R10
6 · The paper itself

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.

Indexed as

Machine LearningWearable Electronic DevicesBayes TheoremHeart RateHumansMaleMonitoring, PhysiologicSupport Vector MachineHeart rate variability (HRV)K-nearest neighbour (KNN)Machine learningStress level detectionSupport vector machine (SVM)

Identifiers

PMID40379805
PMCPMC12084638

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.