Adding liveness detection to the hand geometry scanner

In today\u27s dynamic society, the efficiency of the Biometric Systems has an increasing tendency to replace the classic but obsolete keys and passwords. Hand Geometry Readers are popular biometrics used for Access and Control applications. One of their weaknesses is vulnerability to spoofing using fake hands (latex, play-doh or dead-hands).;The objective of this thesis is to design a feature to be added to the Hand Geometry Scanner in order to detect vitality in the hand, reducing spoofing possibilities.;This thesis demonstrates how the Hand Reader was successfully spoofed and shows the implementation of the live detection feature through an inexpensive but efficient electronic design.;The method used for detection is Photo-Plethysmography. The Reflectance Sensor built is of original conception. After amplifying, filtering and processing the sensor\u27s signal, a message is displayed onto an LCD, concerning the liveness of the hand and the pulse rate

Wearable sensor for continuous monitoring of physiological parameters

Providing high quality health care to a mass population is becoming one of the great endeavors of modern society. In order to do so, there is a urge to embrace the use of new technologies that can provide comfort while ensuring the safety and reliability of traditional methods. The system hereby proposed ought to be capable of monitoring a person's vital signs therefore being very flexible regarding its application scenarios. It can be used not only in emergency wards and screening diseases but also in a home environment to monitor elderly people or young children. Furthermore, it is not exclusive to monitoring and preventing diseases, it can also be an instrument that aids sports training at high intensity levels. This product can measure a patient's heart rate and oxygen saturation levels ensuring comfort and easy usage. Another advantage when compared to traditional machines used to fit the same purpose is the fact that it is much cheaper, takes up less space and it encompasses two functional- ities that are otherwise measured with different machines. This system has two major components, an ESP32 microprocessor and a MAX30100 Pho- toPletysmoGraphy (PPG) sensor. The ESP32 module was chosen due to its computing capacity (dual-core 32-bit processor), having a WiFi module built in with full TCP/IP stack and having 3 pre-defined sleep modes to reduce power consumption. The MAX30100 sensor was picked because it is a compact and small module with simple usage. Furthermore, the goal of this disser- tation is to build this system to be energy efficient, maximizing its battery life while not compro- mising its logical correctness. The configuration chosen that produced steady results whilst consuming lowest energy possi- ble was: 37 mA of current for the IR LED, sampling frequency of 50 Hz and pulse width of 200 μs

Health monitoring system using pulse oximeter with remote alert

This A remote patient monitoring system is implemented which is used for real time monitoring of various heath parameters of a remotely based patient. Oxygen saturation and body temperature are the two parameters calculated and transmitted via a server to a remote client. The main purpose of this paper is to present a remote Pulse Oximetry System for health monitoring purposes. The framework lays on the idea that the vital health signs, can be collected from the patient and passed to a processer, where these signs will be processed, compared and monitored in order to alert important personnel in the case of an emergency. The blood oxygen saturation is the biometric sign which is monitored by this device .The technique used in this work is called “Photoplethysmography” which is based on the change in the intensity of light transmitted through the tissue due to arterial blood pulse. This technique converts the intensity of light into a voltage signal which is used to calculate the oxygen saturation of the patient. This is due to the fact that oxygenated blood has such characteristics in absorbing the Red and Infrared wavelengths which differs from the deoxygenated blood. Comparison of the two absorptions produces an estimation of the oxygen saturation in the patient’s blood

A Simple front-end for pulse oximeters with a direct light-to-time-to-iigital interface circuit

Pulse oximeters are electronic devices that pro- vide the values of two physiological parameters: heart rate (HR) and functional O2 saturation, SpO2. Acquiring this infor- mation requires a photodiode and a set of red and infrared light-emitting diodes (LEDs) to generate two plethysmo- grams, which are digitally processed to find the HR and SpO2 values. The circuits currently used to generate these plethys- mograms require multiple analog modules, such as voltage references, transimpedance amplifiers (TIAs), voltage ampli- fiers, and comparators. Analog-to-digital converters (ADCs) or switched integrators (SIs) are also needed to digitalize the signals that provide the HR and SpO2 values when introduced in a digital processor (DP). This article proposes a new circuit that eliminates the need for all these analog modules or converters, replacing them with a single capacitor and two resistors. The circuit is based on a light-to-time-to-digital conversion performed using the DP, which does not require any special characteristics to carry out this task. As a proof of concept, the new design has been implemented using a field-programmable gate array (FPGA) as the DP. The results show plethysmograms with good detail regarding amplitude and time, allowing the device to be used for clinical purposes. A comparison with two conventional commercial pulse oximeters shows that the new circuit provides similar HR and SpO2 values.Funding for open Access charge: Universidad de Málaga / CBU

A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator

This thesis presents a novel electrocardiogram (ECG) processing algorithm design based on a Multiple Model Adaptive Estimator (MMAE) for a physiological monitoring system. Twenty ECG signals from the MIT ECG database were used to develop system models for the MMAE. The P-wave, QRS complex, and T-wave segments from the characteristic ECG waveform were used to develop hypothesis filter banks. By adding a threshold filter-switching algorithm to the conventional MMAE implementation, the device mimics the way a human analyzer searches the complex ECG signal for a useable temporal landmark and then branches out to find the other key wave components and their timing. The twenty signals and an additional signal from an animal exsanuinaiton experiment were then used to test the algorithm. Using a conditional hypothesis-testing algorithm, the MMAE correctly identified the ECG signal segments corresponding to the hypothesis models with a 96.8% accuracy-rate for the 11539 possible segments tested. The robust MMAE algorithm also detected any misalignments in the filter hypotheses and automatically restarted filters within the MMAE to synchronize the hypotheses with the incoming signal. Finally, the MMAE selects the optimal filter bank based on incoming ECG measurements. The algorithm also provides critical heart-related information such as heart rate, QT, and PR intervals from the ECG signal. This analyzer could be easily added as a software update to the standard physiological monitors universally used in emergency vehicles and treatment facilities and potentially saving thousands of lives and reducing the pain and suffering of the injured

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

Real time perfusion and oxygenation monitoring in an implantable optical sensor

Simultaneous blood perfusion and oxygenation monitoring is crucial for patients undergoing a transplant procedure. This becomes of great importance during the surgical recovery period of a transplant procedure when uncorrected loss of perfusion or reduction in oxygen saturation can result in patient death. Pulse oximeters are standard monitoring devices which are used to obtain the perfusion level and oxygen saturation using the optical absorption properties of hemoglobin. However, in cases of varying perfusion due to hemorrhage, blood clot or acute blockage, the oxygenation results obtained from traditional pulse oximeters are erroneous due to a sudden drop in signal strength. The long term goal of the project is to devise an implantable optical sensor which is able to perform better than the traditional pulse oximeters with changing perfusion and function as a local warning for sudden blood perfusion and oxygenation loss. In this work, an optical sensor based on a pulse oximeter with an additional source at 810nm wavelength has been developed for in situ monitoring of transplant organs. An algorithm has been designed to separate perfusion and oxygenation signals from the composite signal obtained from the three source pulse oximetry-based sensor. The algorithm uses 810nm reference signals and an adaptive filtering routine to separate the two signals which occur at the same frequency. The algorithm is initially applied to model data and its effectiveness is further tested using in vitro and in vivo data sets to quantify its ability to separate the signals of interest. The entire process is done in real time in conjunction with the autocorrelation-based time domain technique. This time domain technique uses digital filtering and autocorrelation to extract peak height information and generate an amplitude measurement and has shown to perform better than the traditional fast Fourier transform (FFT) for semi-periodic signals, such as those derived from heart monitoring. In particular, in this paper it is shown that the two approaches produce comparable results for periodic in vitro perfusion signals. However, when used on semi periodic, simulated, perfusion signals and in vivo data generated from an optical perfusion sensor the autocorrelation approach clearly (Standard Error, SE = 0.03) outperforms the FFT-based analysis (Standard Error, SE = 0.62)

Machine learning algorithm development of SPO2 sensor for improved robustness in wearables

Wearable devices application in the digital measurement of health has gained attention by researchers. These devices allow for data acquisition during real-life activities, resulting in higher data availability. They often include photoplethysmography (PPG) sensors, the sensor behind pulse oximetry. Pulse oximetry is a non-invasive method for continuous oxygen saturation (SpO2) measurements, a standard monitor for anesthesia procedures, an essential tool for managing patients undergoing pulmonary rehabilitation and an effective method for assessing sleep-disordered breathing. However, the current market focuses on heart rate measurements and lacks the robustness of clinical applications for SpO2 assessment. In addition, the most common obstacle in PPG measurements is the signal quality, especially in the form of motion artifacts. Thus, this work aims at increasing the clinical robustness in this devices by evaluating its quality and then extracting relevant metrics. Firstly, a data acquisition protocol was developed, focused on acquiring data during daily activities. This resulted in a dataset with different signal qualities, which was manually annotated to be used as the base for the Machine Learning models. A second protocol was also developed especially designed for the extraction of the SpO2 measurement. Several Machine Learning models were developed to evaluate the signal in three distinct qualities (corrupted, suboptimal, optimal) in real time. A Random Forest classifier achieved accuracies of 79% and 80% for the binary models capable of differentiating between usable and unusable signals, and accuracies of 74% and 80% when distinguishing between optimal and suboptimal signals, for the two utilized channels. The multi-class models achieved accuracies of 66% and 65% for the two utilized channels. Three clinically relevant metrics were also extracted from the PPG signal: heart rate, respiratory rate and SpO2. The heart rate and respiratory rate algorithms resulted in performances similar to the ones found in the literature and in other devices currently on the market. However, while promising, more data is needed to reach statistical significance for the SpO2 measurement.A monitorização do estado de saúde de pacientes em ambulatório utilizando dispositivos wearables tem vindo a ser cada vez mais investigada. Estes dispositivos permitem uma aquisição de dados durante o dia a dia, resultando num maior conjunto de dados. Frequentemente, estes dispositivos incluem fotopletismógrafos (PPG), o sensor por detrás da oximetria de pulso. A oximetria de pulso é um método não invasivo para a medição da saturação de oxigénio no sangue (SpO2) de forma contínua. É um equipamento padrão para procedimentos com anestesia, uma ferramenta essencial para monitorizar pacientes em reabilitação pulmonar e um método eficaz para avaliar respiração desordenada do sono. Ainda assim, o mercado atual foca-se principalmente em medições da frequência cardíaca e carece robustez para aplicações clínicas da medição de SpO2. Para além disso, o obstáculo mais comum em medições com PPG é a qualidade do sinal. Consequentemente, este trabalho procura melhorar a robustez clínica destes dispositivos analisando a qualidade do sinal e, posteriormente, extrair métricas relevantes. Primeiramente, foi desenvolvido um protocolo para aquisição de dados de atividades do dia a dia. Assim, foram adquiridos dados com diferentes qualidades, que foram avaliados manualmente de forma a servir de base para os vários modelos de Machine Learning. Também foi desenvolvido um segundo protocolo para a extração do valor de SpO2. Diferentes modelos de Machine Learning foram desenvolvidos para avaliar em tempo real a qualidade do sinal em três qualidades (corrompido, subótimo, ótimo) . Um classificador baseado em Random Forest atingiu exatidões de 79% e 80% em classificadores binários capazes de distinguir entre sinais úteis e inúteis, e exatidões de 74% e 80% a diferenciar entre um sinal subótimo e ótimo, para os dois canais usados. Os classificadores multi-classe atingiram exatidões de 66% e 65% para os dois canais usados. Três medidas clinicamente relevantes foram também extraídas do sinal de PPG: frequências cardíaca e respiratória, cujos algoritmos atingiram resultados semelhantes aos encontrados na literatura e em aparelhos no mercado, e SpO2 que, ainda que promissores, mais dados seriam necessários para os resultados serem estatisticamente significativo

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces