Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Cluster consistency: Simple yet effect robust learning algorithm on large-scale photoplethysmography for atrial fibrillation detection in the presence of real-world label noise

Obtaining large-scale well-annotated is always a daunting challenge, especially in the medical research domain because of the shortage of domain expert. Instead of human annotation, in this work, we use the alarm information generated from bed-side monitor to get the pseudo label for the co-current photoplethysmography (PPG) signal. Based on this strategy, we end up with over 8 million 30-second PPG segment. To solve the label noise caused by false alarms, we propose the cluster consistency, which use an unsupervised auto-encoder (hence not subject to label noise) approach to cluster training samples into a finite number of clusters. Then the learned cluster membership is used in the subsequent supervised learning phase to force the distance in the latent space of samples in the same cluster to be small while that of samples in different clusters to be big. In the experiment, we compare with the state-of-the-art algorithms and test on external datasets. The results show the superiority of our method in both classification performance and efficiency

Streaming in-patient BPM data to the cloud with a real-time monitoring system

Monitoring the heart activities for old people or people with medical history (Arrhythmia or CHD) is targeted by most new medical technologies. This paper demonstrated an in-patient real-time monitoring system for heart rate estimation. A ratio of beats per minute (BPM) is continuously recorded, streamed and archived to the cloud via WeMos WiFi development board. This cost effective system is simply based on two sub-systems: BPM data acquisition through pulse sensor and WeMos-based communication systems. The streamed BPM data are saved instantaneously in Google drive as spreadsheets which can only be accessed by authorized persons wherever the internet service is available. Thus, the person in charge can remotely observe the patient’s status and do analytics for the archived data. A pilot study with eight subjects was carried out to validate the developed BPM tele-monitoring system. Encouraging results have been achieved

Experimental study for determining the parameters required for detecting ECG and EEG related diseases during the timed-up and go test

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.info:eu-repo/semantics/publishedVersio

Electronic devices and systems for monitoring of diabetes and cardiovascular diseases

Diabetes is a serious chronic disease which causes a high rate of morbidity and mortality all over the world. In 2007, more than 246 million people suffered from diabetes worldwide and unfortunately the incidence of diabetes is increasing at alarming rates. The number of people with diabetes is expected to double within the next 25 years due to a combination of population ageing, unhealthy diets, obesity and sedentary lifestyles. It can lead to blindness, heart disease, stroke, kidney failure, amputations and nerve damage. In women, diabetes can cause problems during pregnancy and make it more likely for the baby to be born with birth defects. Moreover, statistical analysis shows that 75% of diabetic patients die prematurely of cardiovascular disease (CVD). The absolute risk of cardiovascular disease in patients with type 1 (insulin-dependent) diabetes is lower than that in patients with type 2 (non-insulin-dependent) diabetes, in part because of their younger age and the lower prevalence of CVD risk factors, and in part because of the different pathophysiology of the two diseases. Unfortunately, about 9 out of 10 people with diabetes have type 2 diabetes. For these reasons, cardiopathes and diabetic patients need to be frequently monitored and in some cases they could easily perform at home the requested physiological measurements (i.e. glycemia, heart rate, blood pressure, body weight, and so on) sending the measured data to the care staff in the hospital. Several researches have been presented over the last years to address these issues by means of digital communication systems. The largest part of such works uses a PC or complex hardware/software systems for this purpose. Beyond the cost of such systems, it should be noted that they can be quite accessible by relatively young people but the same does not hold for elderly patients more accustomed to traditional equipments for personal entertainment such as TV sets. Wearable devices can permit continuous cardiovascular monitoring both in clinical settings and at home. Benefits may be realized in the diagnosis and treatment of a number of major 15 diseases. In conjunction with appropriate alarm algorithms, they can increase surveillance capabilities for CVD catastrophe for high-risk subjects. Moreover, they could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.) or during sport activities. For patients with chronic cardiovascular disease, such as heart failure, home monitoring employing wearable device and tele-home care systems may detect exacerbations in very early stages or at dangerous levels that necessitate an emergency room visit and an immediate hospital admission. Taking into account mains principles for the design of good wearable devices and friendly tele-home care systems, such as safety, compactness, motion and other disturbance rejection, data storage and transmission, low power consumption, no direct doctor supervision, it is imperative that these systems are easy to use and comfortable to wear for long periods of time. The aim of this work is to develop an easy to use tele-home care system for diabetes and cardiovascular monitoring, well exploitable even by elderly people, which are the main target of a telemedicine system, and wearable devices for long term measuring of some parameters related to sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis. Since set-top boxes for Digital Video Broadcast Terrestrial (DVB-T) are in simple computers with their Operating System, a Java Virtual Machine, a modem for the uplink connection and a set of standard ports for the interfacing with external devices, elderly, diabetics and cardiopathes could easily send their self-made exam to the care staff placed elsewhere. The wearable devices developed are based on the well known photopletysmographic method which uses a led source/detector pair applied on the skin in order to obtain a biomedical signal related to the volume and percentage of oxygen in blood. Such devices investigate the possibility to obtain more information to those usually obtained by this technique (heart rate and percentage of oxygen saturation) in order to discover new algorithms for the continuous and remote or in ambulatory monitoring and screening of sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis

eHealth in hypertension and cardiovascular disease:Opportunities and challenges

In this thesis we investigate different aspects of eHealth for hypertension and cardiovascular disease, with a focus on remote monitoring programs for chronic care. We use the Dutch HartWacht program for patients with hypertension, cardiac arrhythmias and heart failure as an example that has been implemented in routine clinical care. We first focus on hypertension and identify areas that are attractive for future implementation of eHealth because of poor hypertension control. In the following chapters we present economical, legal and technical challenges that accompany eHealth implementation, in each chapter followed by potential solutions and opportunities. We identify success factors for cost-effective eHealth, provide a roadmap for GDPR-compliant solutions, present a novel technique for heartbeat detection through a bracelet and describe a protocol for efficient data handling in remote monitoring programs. In the second part of this thesis, we zoom in on the patients participating in eHealth programs. We evaluate the impact on quality of life of patients participating in the HartWacht program for cardiac arrhythmias and demonstrate equivalence compared to usual care. We then describe the feasibility of the HartWacht program for patients with hypertension in reducing blood pressure and present rationale, design and cohort profile of the Effectiveness of home-Monitoring of blood pressure in PAtients with difficult to Treat HYpertension (EMPATHY) trial. We conclude with an evaluation of the impact of the COVID-19 pandemic on the uptake of eHealth in primary care in the Netherlands

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