Review on Data Acquisition of Electrocardiogram Biometric Recognition in Wearable Smart Textile Shirts

Nawawi, Mohd; Sidek, Khairul Azami; Dafhalla, Alaa K. Y; Azman, Amelia Wong

doi:10.1088/1742-6596/1900/1/012019

Cited by 5 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A review of wearable smart textile shirts is presented in [15]. These shirts have a sensor inside a textile which can record ECG data in a more comfortable way.…”

Section: Related Workmentioning

confidence: 99%

Mobile ECG Devices Selection

Sladojevic,

Anderla,

Gutai

et al. 2024

Teh. glas. (Online)

View full text Add to dashboard Cite

Cardiovascular diseases are the main cause of death across the globe. Therefore, it is critical to detect them at an early stage, while an electrocardiogram (ECG) presents a widely used tool for making diagnosis. There is a large number of ECG devices on the market, with different features that suit a wide range of users, but simultaneously it poses a problem for the end user because they have a difficulty in selecting the appropriate device for their exact needs. Current trends in the usage of contemporary mobile ECG devices are identified and analyzed. In this study authors reviewed numerous mobile ECG devices with either FDA approval or CE mark that are currently available on the market. All listed devices are presented with all technical features that could be found. Furthermore, an interactive sunburst diagram for the selection of mobile ECG devices is created, and published, to help everyone easily make a selection.

show abstract

“…A review of wearable smart textile shirts is presented in [15]. These shirts have a sensor inside a textile which can record ECG data in a more comfortable way.…”

Section: Related Workmentioning

confidence: 99%

Mobile ECG Devices Selection

Sladojevic,

Anderla,

Gutai

et al. 2024

Teh. glas. (Online)

View full text Add to dashboard Cite

show abstract

“…The BCNN is the best classifier and proves that when values with distant temporal intervals are used, there may be a greater difficulty in correctly identifying the subject, because its morphology may be slightly different over time. However, for all cases, and based on the literature [24], it was possible to obtain viable user identification results through ECG signals collected at the thighs.…”

Section: Biometric Identificationmentioning

confidence: 99%

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Silva

Correia

Melo

et al. 2022

Sensors

View full text Add to dashboard Cite

This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24–70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

show abstract