Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Visual Content Privacy Protection: A Survey

Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds of privacy-sensitive information. Researchers have been working continuously to develop targeted privacy protection solutions, and there are several surveys to summarize them from certain perspectives. However, these surveys are either problem-driven, scenario-specific, or technology-specific, making it difficult for them to summarize the existing solutions in a macroscopic way. In this survey, a framework that encompasses various concerns and solutions for visual privacy is proposed, which allows for a macro understanding of privacy concerns from a comprehensive level. It is based on the fact that privacy concerns have corresponding adversaries, and divides privacy protection into three categories, based on computer vision (CV) adversary, based on human vision (HV) adversary, and based on CV \& HV adversary. For each category, we analyze the characteristics of the main approaches to privacy protection, and then systematically review representative solutions. Open challenges and future directions for visual privacy protection are also discussed.Comment: 24 pages, 13 figure

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

A contribution to unobtrusive video-based measurement of respiratory signals

Due to the growing popularity of video-based methods for physiological signal measurement, and taking into account the technological advancements of these type of devices, this work proposes a series of new novel methods to obtain the respiratory signal from a distance, based on video analysis. This thesis aims to improve the state of the art video methods for respiratory measurement, more specifically, by presenting methods that can be used to obtain respiratory variability or perform respiratory rhythm measurements. Moreover, this thesis also aims to present a new implementation of a time-frequency signal processing technique, to improve its computational efficiency when applied to the respiratory signals. In this document a first approach to video-based methods for respiratory signal measurement is performed, to assert the feasibility of using a consumer-grade camera, not only to measure the mean respiratory rate or frequency, but to assert if this hardware could be used to acquire the raw respiratory signal and the respiratory rhythm as well. In this regard a new video-based method was introduced that measures the respiratory signal of a subject at a distance, with the aid of a custom pattern placed on the thorax of the subject. Given the results from the first video-based method, a more broad approach was taken by comparing three different types of video hardware, with the aim to characterise if they could be used for respiratory signal acquisition and respiratory variability measurements. The comparative analysis was performed in terms of instantaneous frequency, as it allowed to characterise the methods in terms of respiratory variability and to compare them in the same terms with the reference method. Subsequently, and due to the previous obtained results, a new method was proposed using a stereo depth camera with the aim to tackle the limitations of the previous study. The proposed method uses an hybrid architecture were the synchronized infrared frame and depth point-cloud from the same camera are acquired. The infrared frame is used to detect the movements of the subject inside the scene, and to recompute on demand a region of interest to obtain the respiratory signal from the depth point-cloud. Furthermore, in this study an opportunistic approach is taken in order to process all the obtained data, as it is also the aim of this study to verify if using a more realistic approach to respiratory signal analysis in real-life conditions, would influence the respiratory rhythm measurement. Even though the depth camera method proved reliable in terms of respiratory rhythm measurement, the opportunistic approach relied on visual inspection of the obtained respiratory signal to properly define each piece. For this reason, a quality indicator had to be proposed that could objectively identify whenever a respiratory signal contained errors. Furthermore, from the idea to characterise the movements of a subject, and by changing the measuring point from a frontal to a lateral perspective to avoid most of the occlusions, a new method based on obtaining the movement of the thoraco-abdominal region using dense optical flow was proposed. This method makes us of the phase of the optical flow to obtain the respiratory signal of the subject, while using the modulus to compute a quality index. Finally, regarding the different signal processing methods used in this thesis to obtain the instantaneous frequency, there were none that could perform in real-time, making the analysis of the respiratory variability not possible in real-life systems where the signals have to be processed in a sample by sample basis. For this reason, as a final chapter a new implementation of the synchrosqueezing transform for time-frequency analysis in real-time is proposed, with the aim to provide a new tool for non-contact methods to obtain the variability of the respiratory signal in real-time.A causa de la creixent popularitat en la mesura de senyals fisiològics amb mètodes de vídeo, i tenint en compte els avenços tecnològics d'aquests dispositius, aquesta tesi proposa una sèrie de nous mètodes per tal d'obtenir la respiració a distància mitjançant l'anàlisi de vídeo. Aquesta tesi té com a objectiu millorar l'estat de l'art referent a la mesura de senyal respiratòria mitjançant els mètodes que en ella es descriuen, així com presentar mètodes que puguin ser usats per obtenir la variabilitat o el ritme respiratori. A més, aquesta tesi té com a objectiu presentar una nova implementació d'un mètode de processat de senyal temps-freqüencial, per tal de millorar-ne l'eficiència computacional quant s’aplica a senyals respiratoris. En aquest document, es realitza una primera aproximació a la mesura de senyal respiratòria mitjançant mètodes de vídeo per tal de verificar si és factible utilitzar una càmera de consum, no només per mesurar el senyal respiratori, sinó verificar si aquest tipus de hardware també pot ser emprat per obtenir el ritme respiratori. En aquest sentit, es presenta en aquest document un nou mètode d'adquisició de senyal respiratòria a distància basat en vídeo, el qual fa ús d'un patró ubicat al tòrax del subjecte per tal d'obtenir-ne la respiració. Un cop obtinguts els resultats del primers resultats, s'han analitzat tres tipus diferents de càmeres, amb la finalitat de caracteritzar-ne la viabilitat d'obtenir el senyal respiratori i la seva variabilitat. L'estudi comparatiu s'ha realitzat en termes de freqüència instantània, donat que permet caracteritzar els mètodes en termes de variabilitat respiratòria i comparar-los, en les mateixes condicions, amb el mètode de referencia. A continuació, s'ha presentat un nou mètode basat en una càmera de profunditat estèreo amb la finalitat de millorar i corregir les limitacions anteriors. El nou mètode proposat es basa en una arquitectura hibrida la qual utilitza els canals de vídeo infraroig i de profunditat de forma sincronitzada. El canal infraroig s'utilitza per detectar els moviments del subjecte dins l'escena i calcular, sota demanda, una regió d'interès que s'utilitza posteriorment en el canal de profunditat per extreure el senyal respiratori. A més a més, en aquest estudi s'ha utilitzat una aproximació oportunista en el processat del senyal respiratori, donat que també és un dels objectius d'aquest estudi, verificar si el fet d'utilitzar una aproximació més realista en l'adquisició de senyal, pot influir en la mesura del ritme respiratori. Tot i que el mètode anterior es mostra fiable en termes de mesura del ritme respiratori, la selecció oportunista del senyal necessita d’inspecció visual per tal de definir correctament cada fragment. Per aquest motiu, era necessari definir un índex de qualitat el qual permetés identificar de forma objectiva cada tram de senyal, així com detectar si el senyal conté errors. Partint de la idea de caracteritzar el moviment del subjecte de l'estudi anterior, i modificant el punt de mesura frontal cap a un de lateral per tal d'evitar oclusions, es proposa un nou mètode basat en l'obtenció del moviment toràcic-abdominal a partir del flux òptic del senyal de vídeo. Aquest mètode recupera el senyal respiratori del subjecte a partir de la fase del flux òptic, tot calculant un índex de qualitat a partir del mòdul. Finalment, i tenint en compte els diferents mètodes de processat utilitzats en aquesta tesi per tal de obtenir la freqüència instantània, es pot apreciar que cap d'ells és capaç de funcionar en temps real, fent inviable l'anàlisi de la variabilitat respiratòria en sistemes reals amb processat mostra a mostra. Per aquest motiu, en el capítol final d'aquesta tesi, s'ha proposat una nova implementació de la transformació "synchrosqueezing" per tal de realitzar l’anàlisi temporal-freqüencial en temps real, i proveir d'una nova eina per tal d'obtenir la variabilitat respiratòria en temps real, amb mètodes sense contacte

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces