CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

Estimating pulse wave velocity using mobile phone sensors

Pulse wave velocity has been recognised as an important physiological phenomenon in the human body, and its measurement can aid in the diagnosis and treatment of chronic diseases. It is the gold standard for arterial stiffness measurements, and it also shares a positive relationship with blood pressure and heart rate. There exist several methods and devices via which it can be measured. However, commercially available devices are more geared towards working health professionals and hospital settings, requiring a significant monetary investment and specialised training to operate correctly. Furthermore, most of these devices are not portable and thus generally not feasible for private home use by the common individual. Given its usefulness as an indicator of certain physiological functions, it is expected that having a more portable, affordable, and simple to use solution would present many benefits to both end users and healthcare professionals alike. This study investigated and developed a working model for a new approach to pulse wave velocity measurement, based on existing methods, but making use of novel equipment. The proposed approach made use of a mobile phone video camera and audio input in conjunction with a Doppler ultrasound probe. The underlying principle is that of a two-point measurement system utilising photoplethysmography and electrocardiogram signals, an existing method commonly found in many studies. Data was collected using the mobile phone sensors and processed and analysed on a computer. A custom program was developed in MATLAB that computed pulse wave velocity given the audio and video signals and a measurement of the distance between the two data acquisition sites. Results were compared to the findings of previous studies in the field, and showed similar trends. As the power of mobile smartphones grows, there exists potential for the work and methods presented here to be fully developed into a standalone mobile application, which would bring forth real benefits of portability and cost-effectiveness to the prospective user base

Computational Approaches for Monitoring of Health Parameters and Their Evaluation for Application in Clinical Setting.

The algorithms and mathematical methods developed in this work focus on using computational approaches for low cost solution of health care problems for better patient outcome. Furthermore, evaluation of those approaches for clinical application considering the risk and benefit in a clinical setting is studied. Those risks and benefits are discussed in terms of sensitivity, specificity and area under the receiver operating characteristics curve. With a rising cost of health care and increasing number of aging population, there is a need for innovative and low cost solutions for health care problems. In this work, algorithms, mathematical techniques for the solutions of the problems related to physiological parameter monitoring have been explored and their evaluation approaches for application in a clinical setting have been studied. The physiological parameters include affective state, pain level, heart rate, oxygen saturation, hemoglobin level and blood pressure. For the mathematical basis development for different data intensive problems, eigenvalue based methods along with others have been used in designing innovative solutions for health care problems, developing new algorithms for smart monitoring of patients; from home monitoring to combat casualty situations. Eigenvalue based methods already have wide applications in many areas such as analysis of stability in control systems, search algorithms (Google Page Rank), Eigenface methods for face recognition, principal component analysis for data compression and pattern recognition. Here, the research work in 1) multi-parameter monitoring of affective state, 2) creating a smart phone based pain detection tool from facial images, 3) early detection of hemorrhage from arterial blood pressure data, 4) noninvasive measurement of physiological signals including hemoglobin level and 5) evaluation of the results for clinical application are presented

A photoplethysmography smartphone-based method for heart rate variability assessment: device model and breathing influences

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A measurement method of heart rate and heart rate variability (HRV) based on smartphone has been developed and validated. The method is based on photoplethysmography (PPG) acquired with the smartphone camera (SPPG). SPPG was compared with the electrocardiogram (ECG), used as the gold standard, and with an external PPG sensor. Twenty-three healthy subjects were measured using two different smartphone models in three different breathing conditions. The error of the first differentiation between SPPG and ECG series is minimized with the fiducial point at maximum first derivative of the SPPG. The obtained standard deviation of error (SDE) between SPPG and ECG is around 5.4 ms and it is similar to SDE between PPG and ECG. Good agreement between SPPG and ECG for NN, SDNN and RMSSD have been found but it is insufficient agreement for LF/HF. Similar levels of agreement for SPPG-ECG and PPG-ECG have been obtained for the HRV indices. Finally, the differences between smartphone models for HRV indices are slight. Therefore, the smartphone can be used for measuring accurately the following HRV indices: NN, SDNN and RMSSD.Peer ReviewedPostprint (published version

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Automated Remote Pulse Oximetry System (ARPOS)

Funding: This research is funded by the School of Computer Science and by St Leonard’s Postgraduate College Doctoral Scholarship, both at the University of St Andrews for Pireh Pirzada’s PhD. Early work was funded by the Digital Health & Care Innovation Centre (DHI).Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B and IR data. Moreover, no formal remote photoplethysmography studies have been done in real life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from Colour, IR and Depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.Publisher PDFPeer reviewe