Gait Verification using Knee Acceleration Signals

A novel gait recognition method for biometric applications is proposed. The approach has the following distinct features. First, gait patterns are determined via knee acceleration signals, circumventing difficulties associated with conventional vision-based gait recognition methods. Second, an automatic procedure to extract gait features from acceleration signals is developed that employs a multiple-template classification method. Consequently, the proposed approach can adjust the sensitivity and specificity of the gait recognition system with great flexibility. Experimental results from 35 subjects demonstrate the potential of the approach for successful recognition. By setting sensitivity to be 0.95 and 0.90, the resulting specificity ranges from 1 to 0.783 and 1.00 to 0.945, respectively

Biometric Authentication Based on Electrocardiogram

The life of modern society is impossible without trust. To ensure trust in the digital world, various encryption algorithms and password policies are used. Passwords are used in a variety of applications from banking applications to email. The advantages of passwords include ease of use and widespread distribution. Forgotten password can be restored or changed. Password weaknesses are largely related to the human factor. Many users use passwords such as “1234” or “qwerty,” and they are also willing to share passwords with friends and colleagues. Vulnerabilities are also associated with software and hardware manufacturers. Many Wi-Fi routers preset very simple passwords, which many users leave unchanged. There are questions for manufacturers of mobile applications. Due to the imperfection of their software, personal data of users often leak. Due to the prevalence of social networks, new authentication methods have appeared. On many websites, you can use accounts from Facebook  or Gmail.com for authentication. If hackers manage to break into large IT vendors, then millions of accounts will be leaked. Many common password problems can be overcome with biometric identification. In particular, biometric data are very difficult to fake; they usually do not change over time. Widespread methods of biometric identification, such as fingerprinting, retina recognition, and voice recognition have various vulnerabilities unfortunately

High School and College Athletes Should Be Required to Undergo Pre-Participation Cardiac Screening Prior to Participation in Competitive Sports

Sudden Cardiac Arrest (SCA) is the leading cause of death in young athletes. Most of these athletes are unaware they have a condition that puts them at risk. In addition it is estimated that approximately 1 in 220,000 young athletes experience Sudden Cardiac Death (SCD) each year, although, these numbers are not truly reliable because there is no national mandatory reporting system in the United States. My paper argues that all high school and college athletes should be required to undergo pre-participation cardiac screening (i.e. an ECG and extensive family health history) as a part of a required physical exam to identify student-athletes at risk of SCD. Studies in Italy, where pre-participation screening is mandatory, and at some US universities in the US where collegiate athletes received cardiovascular screening prior to sports participation suggest that including ECG improved overall sensitivity, mass ECG screening is achievable and cost-effective, and that screening lowered the death rates in the population screened. Some critics believe that the cost of the screening is not cost-effective, but others believe the costs are reasonable; some hospitals in the US now provide student athletes with free ECG screening, or at reduced cost. ECG screening will save lives, and should not be discounted as being too costly. Every parent who has a child participating in school athletics should be informed of the risks, and be given the opportunity to have their child tested. Saving someone’s child is worth the cost of testing. My interest in this topic comes from my family’s personal experience with a student athlete who experienced a cardiac event characterized by shortness of breath and dizziness while playing in a collegiate baseball game. Our son, Neil, was a healthy 21-year-old student athlete who had participated in organized sports since he was seven years old. His event was initially diagnosed as a panic attack, but after he underwent a series of cardiac testing, including an ECG that showed an abnormality, he was diagnosed with Arrhythmogenic Right Ventricular Dysplasia (ARVD), a progressive heart disease. We have no family history to link to Neil’s disease, and we had no idea he was at risk. Today Neil lives with an implantable cardioverter defibrillator (ICD), takes anti-arrhythmic medication, and no longer participates in team sports. Neil is one of the lucky ones who survived, and his survival impelled me to get involved in my community to promote awareness about sudden cardiac arrest, and the importance of CPR training and the availability of automated external defibrillators (AEDs). Our experience also inspired me to research adding pre-participation cardiac screening for young athletes. If such a program had been instituted at his high school or college, Neil’s disease would have been diagnosed and treated, keeping him from being at risk during athletics

Non Contact Heart Monitoring

Electrocardiograms are one of the most widely used methods for evaluating the structure-function relationships of the heart in health and disease. This book is the first of two volumes which reviews recent advancements in electrocardiography. This volume lays the groundwork for understanding the technical aspects of these advancements. The five sections of this volume, Cardiac Anatomy, ECG Technique, ECG Features, Heart Rate Variability and ECG Data Management, provide comprehensive reviews of advancements in the technical and analytical methods for interpreting and evaluating electrocardiograms. This volume is complemented with anatomical diagrams, electrocardiogram recordings, flow diagrams and algorithms which demonstrate the most modern principles of electrocardiography. The chapters which form this volume describe how the technical impediments inherent to instrument-patient interfacing, recording and interpreting variations in electrocardiogram time intervals and morphologies, as well as electrocardiogram data sharing have been effectively overcome. The advent of novel detection, filtering and testing devices are described. Foremost, among these devices are innovative algorithms for automating the evaluation of electrocardiograms. Permanenet links: Full chapter: http://www.intechopen.com/articles/show/title/non-contact-heart-monitoring Book: http://www.intechopen.com/books/show/title/advances-in-electrocardiograms-methods-and-analysi

Identification of weakly coupled multiphysics problems. Application to the inverse problem of electrocardiography

This work addresses the inverse problem of electrocardiography from a new perspective, by combining electrical and mechanical measurements. Our strategy relies on the defini-tion of a model of the electromechanical contraction which is registered on ECG data but also on measured mechanical displacements of the heart tissue typically extracted from medical images. In this respect, we establish in this work the convergence of a sequential estimator which combines for such coupled problems various state of the art sequential data assimilation methods in a unified consistent and efficient framework. Indeed we ag-gregate a Luenberger observer for the mechanical state and a Reduced Order Unscented Kalman Filter applied on the parameters to be identified and a POD projection of the electrical state. Then using synthetic data we show the benefits of our approach for the estimation of the electrical state of the ventricles along the heart beat compared with more classical strategies which only consider an electrophysiological model with ECG measurements. Our numerical results actually show that the mechanical measurements improve the identifiability of the electrical problem allowing to reconstruct the electrical state of the coupled system more precisely. Therefore, this work is intended to be a first proof of concept, with theoretical justifications and numerical investigations, of the ad-vantage of using available multi-modal observations for the estimation and identification of an electromechanical model of the heart

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice