198 research outputs found
Identifikasi Personal Biometrik Berdasarkan Sinyal Photoplethysmography dari Detak Jantung
Sistem biometrik sangat berguna untuk membedakan karakteristik individu
seseorang. Sistem identifikasi yang paling banyak digunakan diantaranya berdasarkan
metode fingerprint, face detection, iris atu hand geometry. Penelitian ini mencoba
untuk meningkatkan sistem biometrik menggunakan sinyal Photoplethysmography
dari detak jantung. Algoritma yang diusulkan menggunakan seluruh ektraksi fitur
yang didapatkan melalui sistem untuk pengenalan biometrik. Efesiensi dari algoritma
yang diusulkan didemonstrasikan oleh hasil percobaan yang didapatkan menggunakan
metode klasifikasi Multilayer Perceptron, NaĂŻve Bayes dan Random Forest
berdasarkan fitur ekstraksi yang didapatkan dari proses sinyal prosesing. Didapatkan
51 subjek pada penelitian ini; sinyal PPG signals dari setiap individu didapatkan
melalui sensor pada dua rentang waktu yang berbeda. 30 fitur karakteristik didapatkan
dari setiap periode dan kemudian digunakan untuk proses klasifikasi. Sistem
klasifikasi menggunakan metode Multilayer Perceptron, NaĂŻve Bayes dan Random
Forest; nilai true positive dari masing-masing metode adalah 94.6078 %, 92.1569 %
dan 90.3922 %. Hasil yang didapatkan menunjukkan bahwa seluruh algoritma yang
diusulkan dan sistem identifikasi biometrik dari pengembangan sinyal PPG ini sangat menjanjikan untuk sistem pengenalan individu manusia.
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The importance of biometric system can distinguish the uniqueness of personal
characteristics. The most popular identification systems have concerned the method
based on fingerprint, face detection, iris or hand geometry. This study is trying to
improve the biometric system using Photoplethysmography signal by heart rate. The
proposed algorithm calculates the contribution of all extracted features to biometric
recognition. The efficiency of the proposed algorithms is demonstrated by the
experiment results obtained from the Multilayer Perceptron, NaĂŻve Bayes and
Random Forest classifier applications based on the extracted features. There are fifty
one persons joined for the experiments; the PPG signals of each person were recorded
for two different time spans. 30 characteristic features were extracted for each period
and these characteristic features are used for the purpose of classification. The results
were evaluated via the Multilayer Perceptron, NaĂŻve Bayes and Random Forest
classifier models; the true positive rates are then 94.6078 %, 92.1569 % and 90.3922
%, respectively. The obtained results showed that both the proposed algorithm and the
biometric identification model based on this developed PPG signal are very promising
for contact less recognizing systems
Evaluation of PPG Biometrics for Authentication in different states
Amongst all medical biometric traits, Photoplethysmograph (PPG) is the
easiest to acquire. PPG records the blood volume change with just combination
of Light Emitting Diode and Photodiode from any part of the body. With IoT and
smart homes' penetration, PPG recording can easily be integrated with other
vital wearable devices. PPG represents peculiarity of hemodynamics and
cardiovascular system for each individual. This paper presents non-fiducial
method for PPG based biometric authentication. Being a physiological signal,
PPG signal alters with physical/mental stress and time. For robustness, these
variations cannot be ignored. While, most of the previous works focused only on
single session, this paper demonstrates extensive performance evaluation of PPG
biometrics against single session data, different emotions, physical exercise
and time-lapse using Continuous Wavelet Transform (CWT) and Direct Linear
Discriminant Analysis (DLDA). When evaluated on different states and datasets,
equal error rate (EER) of - was achieved for -s average
training time. Our CWT/DLDA based technique outperformed all other
dimensionality reduction techniques and previous work.Comment: Accepted at 11th IAPR/IEEE International Conference on Biometrics,
2018. 6 pages, 6 figure
Smart aging : utilisation of machine learning and the Internet of Things for independent living
Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves
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A Survey of Wearable Biometric Recognition Systems
The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work
A survey of wearable biometric recognition systems
The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner's physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY) and the CAM Grant S2013/ICE-3095 (CIBERDINE
Seeing Red: PPG Biometrics Using Smartphone Cameras
In this paper, we propose a system that enables photoplethysmogram
(PPG)-based authentication by using a smartphone camera. PPG signals are
obtained by recording a video from the camera as users are resting their finger
on top of the camera lens. The signals can be extracted based on subtle changes
in the video that are due to changes in the light reflection properties of the
skin as the blood flows through the finger. We collect a dataset of PPG
measurements from a set of 15 users over the course of 6-11 sessions per user
using an iPhone X for the measurements. We design an authentication pipeline
that leverages the uniqueness of each individual's cardiovascular system,
identifying a set of distinctive features from each heartbeat. We conduct a set
of experiments to evaluate the recognition performance of the PPG biometric
trait, including cross-session scenarios which have been disregarded in
previous work. We found that when aggregating sufficient samples for the
decision we achieve an EER as low as 8%, but that the performance greatly
decreases in the cross-session scenario, with an average EER of 20%.Comment: 8 pages, 15th IEEE Computer Society Workshop on Biometrics 202
A Survey of PPG's Application in Authentication
Biometric authentication prospered because of its convenient use and
security. Early generations of biometric mechanisms suffer from spoofing
attacks. Recently, unobservable physiological signals (e.g.,
Electroencephalogram, Photoplethysmogram, Electrocardiogram) as biometrics
offer a potential remedy to this problem. In particular, Photoplethysmogram
(PPG) measures the change in blood flow of the human body by an optical method.
Clinically, researchers commonly use PPG signals to obtain patients' blood
oxygen saturation, heart rate, and other information to assist in diagnosing
heart-related diseases. Since PPG signals contain a wealth of individual
cardiac information, researchers have begun to explore their potential in cyber
security applications. The unique advantages (simple acquisition, difficult to
steal, and live detection) of the PPG signal allow it to improve the security
and usability of the authentication in various aspects. However, the research
on PPG-based authentication is still in its infancy. The lack of
systematization hinders new research in this field. We conduct a comprehensive
study of PPG-based authentication and discuss these applications' limitations
before pointing out future research directions.Comment: Accepted by Computer & Security (COSE
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated
directly from the Blood pressure (BP). Hypertension always leads to other
health complications. Continuous monitoring of BP is very important; however,
cuff-based BP measurements are discrete and uncomfortable to the user. To
address this need, a cuff-less, continuous and a non-invasive BP measurement
system is proposed using Photoplethysmogram (PPG) signal and demographic
features using machine learning (ML) algorithms. PPG signals were acquired from
219 subjects, which undergo pre-processing and feature extraction steps. Time,
frequency and time-frequency domain features were extracted from the PPG and
their derivative signals. Feature selection techniques were used to reduce the
computational complexity and to decrease the chance of over-fitting the ML
algorithms. The features were then used to train and evaluate ML algorithms.
The best regression models were selected for Systolic BP (SBP) and Diastolic BP
(DBP) estimation individually. Gaussian Process Regression (GPR) along with
ReliefF feature selection algorithm outperforms other algorithms in estimating
SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively.
This ML model can be implemented in hardware systems to continuously monitor BP
and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table
Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry
In the first half of the 20th century, a first pulse oximeter was available
to measure blood flow changes in the peripheral vascular net. However, it was
not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor
many physiological parameters in clinical environments. Over the last decade,
its use has extended to the area of biometrics, with different methods that
allow the extraction of characteristic features of each individual from the PPG
signal morphology, highly varying with time and the physical states of the
subject. In this paper, we present a novel PPG-based biometric authentication
system based on convolutional neural networks. Contrary to previous approaches,
our method extracts the PPG signal's biometric characteristics from its
diffusive dynamics, characterized by geometric patterns image in the (p,
q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal
are strongly dependent on the vascular bed's biostructure, which is unique to
each individual, and highly stable over time and other psychosomatic
conditions. Besides its robustness, our biometric method is anti-spoofing,
given the convoluted nature of the blood network. Our biometric authentication
system reaches very low Equal Error Rates (ERRs) with a single attempt, making
it possible, by the very nature of the envisaged solution, to implement it in
miniature components easily integrated into wearable biometric systems.Comment: 18 pages, 6 figures, 4 table
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