ArticleComputational and mathematical methods in medicine2015
Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- Effect of a Smart Clothes-Assisted Care System for Persons Living With Dementia on Family Caregivers: Longitudinal Nonblinded Quasi-Experimental Study.Journal of medical Internet research · 2025Article
- Implementation difficulties and solutions for a smart-clothes assisted home nursing care program for older adults with dementia or recovering from hip fracture.BMC medical informatics and decision making · 2024Article
- ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges.Sensors (Basel, Switzerland) · 2023Article
- IoT garment for remote elderly care network.Biomedical signal processing and control · 2021Article
- Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing.Journal of medical and biological engineering · 2017Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the "very good signal" interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis.
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Registered trials
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.