miBEAT Based Continuous and Robust Biometric Identification System for On-the-Go Applications

Yathav, Jayasubha; Bailur, Abhijith; Goyal, Amit; Abhinav, .

doi:10.1007/978-981-10-2750-5_28

Cited by 5 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The photoplethysmography (PPG) sensor measures heart-rate based on detecting blood volume changes in the vessels [5]. A research study has demonstrated that there is a high similarity between heart rate signals/values measured by ECG and PPG devices [118]. Therefore, it is a suitable alternative of ECG that employs a simple optical technology.…”

Section: Photoplethysmography (Ppg) Monitoringmentioning

confidence: 99%

Biometric Systems Utilising Health Data from Wearable Devices

et al. 2020

View full text Add to dashboard Cite

Health data is being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also presents opportunities within computer security, specifically w ith b iometric s ystems u sed f or i dentification an d authentication purposes. This paper performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work. CCS Concepts: • Security and privacy → Biometrics.

show abstract

Section: Photoplethysmography (Ppg) Monitoringmentioning

confidence: 99%

Biometric Systems Utilising Health Data from Wearable Devices

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The features of the 20 PPG signals did not change, and the k-nearest-neighbor (K-NN) classifier was used to achieve a 95% recognition success rate with 10 new features of 10× cross-validation. Yathav et al [19] developed a system called miBEAT that can simultaneously collect ECG and PPG signals and can be used on smartphones or tablets for signal acquisition and personal identification or authentication.…”

Section: Related Workmentioning

confidence: 99%

A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition

Xiao

Song

et al. 2019

Sensors

View full text Add to dashboard Cite

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

show abstract

“…Innovative applications for ECG are emerging, such as subject identification [7][8][9] and detection of drowsiness or fatigue signs in vehicle drivers [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…These conditions make signal acquisition more susceptible to interferences [12,13]. Examples include smartwatches, smartphones, tablets [8], remote controls, bicycle and motorcycle steering forks, vehicle steering wheels [9,14], running belt's handles and computer keyboards [15]. Electrodes can also be integrated in wearable accessories like wristbands [16] or pieces of clothing [7,17,18].…”

Section: Introductionmentioning

confidence: 99%

ECG simulator with configurable skin-electrode impedance and artifacts emulation

Almeida¹,

Costa²,

Lourenço³

2021

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Electrocardiograms (ECG) recorded from everyday objects, such as wearables, fitness machines or smart steering wheels are becoming increasingly common. Applications are diverse and include health monitoring, athletic performance optimization, identification, authentication, and entertainment. In this study we report the design and implementation of an innovative ECG simulator, providing simulation of signal related artifacts and a dynamically adjustable skin-electrode interface model. The ECG simulator includes a unique combination of features: emulation of time dependent skinelectrode impedance, adjustable differential and common-mode interference, generation of lead-off events and analog front-end output digitalization. The skin-electrode capacitance range is 1 nF-255 nF and the resistance span is 4 kΩ-996 kΩ. System's functionality is demonstrated using a commercially available ECG front-end. The simulated SNR degradation introduced by the ECG simulator is under 0.1 dB. Results show that the skin-electrode interface can have a significant impact in the acquired waveforms. Impedance electrode imbalance, specifically of the resistive component, can generate artifacts which can be misinterpreted has arrhythmias. The proposed device can be useful for hardware and software ECG development and for training physicians and nurses to readily recognize skin-electrode impedance related artifacts.

show abstract

miBEAT Based Continuous and Robust Biometric Identification System for On-the-Go Applications

Cited by 5 publications

References 5 publications

Biometric Systems Utilising Health Data from Wearable Devices

Biometric Systems Utilising Health Data from Wearable Devices

A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition

ECG simulator with configurable skin-electrode impedance and artifacts emulation

Contact Info

Product

Resources

About