Healthy and Happy? An Ethical Investigation of Emotion Recognition and Regulation Technologies (ERR) within Ambient Assisted Living (AAL).

Ambient Assisted Living (AAL) refers to technologies that track daily activities of persons in need of care to enhance their autonomy and minimise their need for assistance. New technological developments show an increasing effort to integrate automated emotion recognition and regulation (ERR) into AAL systems. These technologies aim to recognise emotions via different sensors and, eventually, to regulate emotions defined as "negative" via different forms of intervention. Although these technologies are already implemented in other areas, AAL stands out by its tendency to enable an inconspicuous 24-hour surveillance in the private living space of users who rely on the technology to maintain a certain degree of independence in their daily activities. The combination of both technologies represents a new dimension of emotion recognition in a potentially vulnerable group of users. Our paper aims to provide an ethical contextualisation of the novel combination of both technologies. We discuss different concepts of emotions, namely Basic Emotion Theory (BET) and the Circumplex Model of Affect (CMA), that form the basis of ERR and provide an overview over the current technological developments in AAL. We highlight four ethical issues that specifically arise in the context of ERR in AAL systems, namely concerns regarding (1) the reductionist view of emotions, (2) solutionism as an underlying assumption of these technologies, (3) the privacy and autonomy of users and their emotions, (4) the tendency of machine learning techniques to normalise and generalise human behaviour and emotional reactions.

Plant lectins as versatile tools to fight coronavirus outbreaks.

The S protein forming the homotrimeric spikes of pathogenic beta-coronaviruses, such as MERS-CoV, SARS-CoV and SARS-CoV-2, is a highly glycosylated protein containing mainly N-glycans of the complex and high-mannose type, as well as O-glycans. Similarly, the host cell receptors DPP4 for MERS-CoV and ACE2 for SARS-CoV and SARS-CoV-2, also represent N- and O-glycosylated proteins. All these glycoproteins share common glycosylation patterns, suggesting that plant lectins with different carbohydrate-binding specificities could be used as carbohydrate-binding agents for the spikes and their receptors, to combat COVID19 pandemics. The binding of plant lectins to the spikes and their receptors could mask the non-glycosylated receptor binding domain of the virus and the corresponding region of the receptor, thus preventing a proper interaction of the spike proteins with their receptors. In this review, we analyze (1) the ability of plant lectins to interact with the N- and O-glycans present on the spike proteins and their receptors, (2) the in vitro and in vivo anti-COVID19 activity already reported for plant lectins and, (3) the possible ways for delivery of lectins to block the spikes and/or their receptors.

Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes.

An aged population, increasing care needs, and a lack of (in)formal caregivers represent major challenges for our society today. Addressing these challenges fuels efforts and developments in innovative technologies leading to various existing AAL applications aiming at improving autonomy, independence, and security in older age. Here, the usage of video-based AAL technologies is promising as detailed information can be obtained and analyzed. Simultaneously, this type of technology is strongly connected with privacy concerns due to fears of unauthorized data access or inappropriate use of recorded data potentially resulting in rejection and non-use of the applications. As privacy-preserving visualizations of video data can diminish those concerns, this empirical study examines the acceptance and privacy perceptions of video-based AAL technology applying different visualization modes for privacy preservation (n = 161). These visualization modes differed in their degrees of visibility and identifiability, covering different levels of privacy preservation (low level: "Blurred" mode; medium level: "Pixel" and "Grey" modes; high level: "Avatar" mode) and are specifically evaluated based on realistic video sequences. The results of our study indicate a rather low acceptance of video-based AAL technology in general. From the diverse visualization modes, the "Avatar" mode is most preferred as it is perceived as best suitable to protect and preserve the users' privacy. Beyond that, distinct clusters of future users were identified differing in their technology evaluation as well as in individual characteristics (i.e., privacy perception, technology commitment). The findings support the understanding of potential users' needs for a successful future design, development, and implementation of video-based, but still privacy-preserving AAL technology.

IoT Smart Flooring Supporting Active and Healthy Lifestyles.

The lack of physical exercise is among the most relevant factors in developing health issues, and strategies to incentivize active lifestyles are key to preventing these issues. The PLEINAIR project developed a framework for creating outdoor park equipment, exploiting the IoT paradigm to build "Outdoor Smart Objects" (OSO) for making physical activity more appealing and rewarding to a broad range of users, regardless of their age and fitness. This paper presents the design and implementation of a prominent demonstrator of the OSO concept, consisting of a smart, sensitive flooring, based on anti-trauma floors commonly found in kids playgrounds. The floor is equipped with pressure sensors (piezoresistors) and visual feedback (LED-strips), to offer an enhanced, interactive and personalized user experience. OSOs exploit distributed intelligence and are connected to the Cloud infrastructure by using a MQTT protocol; apps have then been developed for interacting with the PLEINAIR system. Although simple in its general concept, several challenges must be faced, related to the application range (which called for high pressure sensitivity) and the scalability of the approach (requiring to implement a hierarchical system architecture). Some prototypes were fabricated and tested in a public environment, providing positive feedback to both the technical design and the concept validation.

Antibiotic allergy labels and optimal antimicrobial stewardship.

BACKGROUND: Although common, antimicrobial allergy labels (AAL) rarely reflect immunologically-mediated hypersensitivity and can lead to poorer outcomes from alternative antimicrobial agents. Antimicrobial stewardship programs are ideally placed to assess AAL early as a means of improving antimicrobial use. AIMS: To quantify the prevalence of AAL in patients referred for antimicrobial stewardship review and assess their impact on antibiotic prescribing, patient mortality, hospital length of stay, readmission and rates of multidrug-resistant infections. METHODS: We conducted a retrospective analysis of adult patients referred for inpatient antimicrobial prospective audit and feedback rounds (PAFR) through an electronic referral system (eReferrals) over a 12-month period in 2015. Outcome data were collected for a period of 36 months following the initial review. RESULTS: Of the 639 patient records reviewed, 630 met inclusion criteria; 103 (16%) had an AAL, of which 82 (13%) had reported allergies to ß-lactam antibiotics. Those with AAL were significantly less likely to be receiving guideline-recommended antimicrobial therapy (50% vs 64%, P = 0.0311); however, there were no significant difference in mortality, hospital length of stay, readmission or increased incidence of multidrug-resistant infections. CONCLUSIONS: Our cohort demonstrated that AAL was associated with reduced adherence to antibiotic guidelines. The lack of association with adverse outcomes may reflect limitations within the study including retrospective cohort study numbers and observational nature, further skewed by high rates of poor documentation. A clear opportunity exists for antimicrobial stewardship programs to incorporate allergy assessment, de-labelling, challenge and referral into these rounds.

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

BACKGROUND: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. OBJECTIVE: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. METHODS: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. RESULTS: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. CONCLUSIONS: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.

Design and Development of a Heterogeneous Active Assisted Living Solution for Monitoring and Following Up with Chronic Heart Failure Patients in Spain.

Heart failure is the most common disease among elderly people, and the risk increases with age. The use of smart Internet of Things (IoT) systems for monitoring patients with chronic heart failure (CHF) in a non-intrusive manner can result in better control of the disease, improving proactive healthcare through real-time and historical patient's data, promoting self-care in patients, reducing unneeded interaction between patients and doctors, reducing the number of hospitalizations and saving healthcare costs. This work presents an active assisted living (AAL) solution based on the IoT to provide a tele-assistance platform for CHF patients from the public health service of the region of Murcia in Spain, with formal and informal caregivers and health professionals also as key actors. In this article, we have detailed the methodology, results, and conclusions of the prevalidation phase for the set of IoT technologies to be integrated in the AAL platform, the first mandatory step before the deployment of a large-scale pilot that will lead to improving the innovation of the system from its current technology readiness level to the market. The work presented, in the framework of the H2020 Pharaon project, aims to serve as inspiration to the R&D community for the design, development, and deployment of AAL solutions based on heterogeneous IoT technologies, or similar approaches, for smart healthcare solutions in real healthcare institutions.

Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults.

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.

Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.

Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach for assessing persons with Alzheimer's disease (AD) mild cognitive impairment (MCI), compared with normal control (NC) persons, using the zoom-in neural network (ZNN) deep-learning algorithm. ZNN stacks a set of zoom-in learning units (ZLUs) in a feedforward hierarchy without backpropagation. The resting-state fMRI (rs-fMRI) dataset for AD assessments was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The Automated Anatomical Labeling (AAL-90) atlas, which provides 90 neuroanatomical functional regions, was used to assess and detect the implicated regions in the course of AD. The features of the ZNN are extracted from the 140-time series rs-fMRI voxel values in a region of the brain. ZNN yields the three classification accuracies of AD versus MCI and NC, NC versus AD and MCI, and MCI versus AD and NC of 97.7%, 84.8%, and 72.7%, respectively, with the seven discriminative regions of interest (ROIs) in the AAL-90.

A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives.

The increasing isolation of the elderly both in their own homes and in care homes has made the problem of caring for elderly people who live alone an urgent priority. This article presents a proposed design for a heterogeneous multirobot system consisting of (i) a small mobile robot to monitor the well-being of elderly people who live alone and suggest activities to keep them positive and active and (ii) a domestic mobile manipulating robot that helps to perform household tasks. The entire system is integrated in an automated home environment (AAL), which also includes a set of low-cost automation sensors, a medical monitoring bracelet and an Android application to propose emotional coaching activities to the person who lives alone. The heterogeneous system uses ROS, IoT technologies, such as Node-RED, and the Home Assistant Platform. Both platforms with the home automation system have been tested over a long period of time and integrated in a real test environment, with good results. The semantic segmentation of the navigation and planning environment in the mobile manipulator for navigation and movement in the manipulation area facilitated the tasks of the later planners. Results about the interactions of users with the applications are presented and the use of artificial intelligence to predict mood is discussed. The experiments support the conclusion that the assistance robot correctly proposes activities, such as calling a relative, exercising, etc., during the day, according to the user's detected emotional state, making this is an innovative proposal aimed at empowering the elderly so that they can be autonomous in their homes and have a good quality of life.

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends.

The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems-ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments.

This paper presents a new architecture that integrates Internet of Things (IoT) devices, service robots, and users in a smart assistive environment. A new intuitive and multimodal interaction system supporting people with disabilities and bedbound patients is presented. This interaction system allows the user to control service robots and devices inside the room in five different ways: touch control, eye control, gesture control, voice control, and augmented reality control. The interaction system is comprised of an assistive robotic arm holding a tablet PC. The robotic arm can place the tablet PC in front of the user. A demonstration of the developed technology, a prototype of a smart room equipped with home automation devices, and the robotic assistive arm are presented. The results obtained from the use of the various interfaces and technologies are presented in the article. The results include user preference with regard to eye-base control (performing clicks, and using winks or gaze) and the use of mobile phones over augmented reality glasses, among others.

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach.

Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household's power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.

Exhaustive Description of the System Architecture and Prototype Implementation of an IoT-Based eHealth Biometric Monitoring System for Elders in Independent Living.

In this paper, we present an exhaustive description of an extensible e-Health Internet-connected embedded system, which allows the measurement of three biometric parameters: pulse rate, oxygen saturation and temperature, via several wired and wireless sensors residing to the realm of Noncommunicable Diseases (NCDs) and cognitive assessment through Choice Reaction Time (CRT) analysis. The hardware used is based on ATMEGA AVR + MySignals Hardware printed circuit board (Hardware PCB), but with multiple upgrades (including porting from ATMEGA328P to ATMEGA2560). Multiple software improvements were made (by writing high-level device drivers, text-mode and graphic-mode display driver) for increasing functionality, portability, speed, and latency. A top-level embedded application was developed and benchmarked. A custom wireless AT command firmware was developed, based on ESP8266 firmware to allow AP-mode configuration and single-command JavaScript Object Notation (JSON) data-packet pushing towards the cloud platform. All software is available in a git repository, including the measurement results. The proposed eHealth system provides with specific NCDs and cognitive views fostering the potential to exploit correlations between physiological and cognitive data and to generate predictive analysis in the field of eldercare.

Identification of Fucosylated SERPINA1 as a Novel Plasma Marker for Pancreatic Cancer Using Lectin Affinity Capture Coupled with iTRAQ-Based Quantitative Glycoproteomics.

Pancreatic cancer (PC) is an aggressive cancer with a high mortality rate, necessitating the development of effective diagnostic, prognostic and predictive biomarkers for disease management. Aberrantly fucosylated proteins in PC are considered a valuable resource of clinically useful biomarkers. The main objective of the present study was to identify novel plasma glycobiomarkers of PC using the iTRAQ quantitative proteomics approach coupled with Aleuria aurantia lectin (AAL)-based glycopeptide enrichment and isotope-coded glycosylation site-specific tagging, with a view to analyzing the glycoproteome profiles of plasma samples from patients with non-metastatic and metastatic PC and gallstones (GS). As a result, 22 glycopeptides with significantly elevated levels in plasma samples of PC were identified. Fucosylated SERPINA1 (fuco-SERPINA1) was selected for further validation in 121 plasma samples (50 GS and 71 PC) using an AAL-based reverse lectin ELISA technique developed in-house. Our analyses revealed significantly higher plasma levels of fuco-SERPINA1 in PC than GS subjects (310.7 ng/mL v.s. 153.6 ng/mL, p = 0.0114). Elevated fuco-SERPINA1 levels were associated with higher TNM stage (p = 0.024) and poorer prognosis for overall survival (log-rank test, p = 0.0083). The increased plasma fuco-SERPINA1 levels support the utility of this protein as a novel prognosticator for PC.

Metabolic Adaptation of Airway Smooth Muscle Cells to an SPHK2 Substrate Precedes Cytostasis.

Thickening of the airway smooth muscle is central to bronchial hyperreactivity. We have shown that the sphingosine analog (R)-2-amino-4-(4-heptyloxyphenyl)-2-methylbutanol (AAL-R) can reverse preestablished airway hyperreactivity in a chronic asthma model. Because sphingosine analogs can be metabolized by SPHK2 (sphingosine kinase 2), we investigated whether this enzyme was required for AAL-R to perturb mechanisms sustaining airway smooth muscle cell proliferation. We found that AAL-R pretreatment reduced the capacity of live airway smooth muscle cells to use oxygen for oxidative phosphorylation and increased lactate dehydrogenase activity. We also determined that SPHK2 was upregulated in airway smooth muscle cells bearing the proliferation marker Ki67 relative to their Ki67-negative counterpart. Comparing different stromal cell subsets of the lung, we found that high SPHK2 concentrations were associated with the ability of AAL-R to inhibit metabolic activity assessed by conversion of the tetrazolium dye MTT. Knockdown or pharmacological inhibition of SPHK2 reversed the effect of AAL-R on MTT conversion, indicating the essential role for this kinase in the metabolic perturbations induced by sphingosine analogs. Our results support the hypothesis that increased SPHK2 levels in proliferating airway smooth muscle cells could be exploited to counteract airway smooth muscle thickening with synthetic substrates.

Automated anatomical labelling atlas 3.

Following a first version AAL of the automated anatomical labeling atlas (Tzourio-Mazoyer et al., 2002), a second version (AAL2) (Rolls et al., 2015) was developed that provided an alternative parcellation of the orbitofrontal cortex following the description provided by Chiavaras, Petrides, and colleagues. We now provide a third version, AAL3, which adds a number of brain areas not previously defined, but of interest in many neuroimaging investigations. The 26 new areas in the third version are subdivision of the anterior cingulate cortex into subgenual, pregenual and supracallosal parts; subdivision of the thalamus into 15 parts; the nucleus accumbens, substantia nigra, ventral tegmental area, red nucleus, locus coeruleus, and raphe nuclei. The new atlas is available as a toolbox for SPM, and can be used with MRIcron.

Overexpression of FBR41 enhances resistance to sphinganine analog mycotoxin-induced cell death and Alternaria stem canker in tomato.

Fumonisin B1 (FB1) and Alternaria alternate f. sp. lycopersici (AAL)-toxin are classified as sphinganine analog mycotoxins (SAMTs), which induce programmed cell death (PCD) in plants and pose health threat to humans who consume the contaminated crop products. Herein, Fumonisin B1 Resistant41 (FBR41), a dominant mutant allele, was identified by map-based cloning of Arabidopsis FB1-resistant mutant fbr41, then ectopically expressed in AAL-toxin sensitive tomato (Solanum lycopersicum) cultivar. FBR41-overexpressing tomato plants exhibited less severe cell death phenotype upon AAL-toxin treatment. Analysis of free sphingoid bases showed that both fbr41 and FBR41-overexpressing tomato plants accumulated less sphinganine and phytosphingosine upon FB1 and AAL-toxin treatment, respectively. Alternaria stem canker is a disease caused by AAL and responsible for severe economic losses in tomato production, and FBR41-overexpressing tomato plants exhibited enhanced resistance to AAL with decreased fungal biomass and less cell death, which was accompanied by attenuated accumulation of free sphingoid bases and jasmonate (JA). Taken together, our results indicate that FBR41 is potential in inhibiting SAMT-induced PCD and controlling Alternaria stem canker in tomato.

UWB/BLE Tracking System for Elderly People Monitoring.

Localization systems are the source of data that allows to evaluate elderly person's behaviour, to draw conclusions concerning his or her health status and wellbeing, and to detect emergency situations. The article contains a description of a system intended for elderly people tracking. Two novel solutions have been implemented in the system: a hybrid localization algorithm and a method for wireless anchor nodes synchronization. The algorithm fuses results of time difference of arrival and received signal strength measurements in ultrawideband (UWB) and Bluetooth Low Energy (BLE) radio interfaces, respectively. The system allows to change the intensity of UWB packets transmission to adapt localization accuracy and energy usage to current needs and applications. In order to simplify the system installation, communication between elements of the system infrastructure instead of wire interfaces is performed over wireless ones. The new wireless synchronization method proposed in the article consists in retransmission of UWB synchronization packets by selected anchor nodes. It allows for extension of the system coverage, which is limited by the short range of UWB transmission. The proposed solution was experimentally verified. The synchronization method was tested in a laboratory, and the whole system's performance was investigated in a typical flat. Exemplary results of the tests performed with older adult participation in their own homes are also included.

An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations.

This work tests the ability of fmcw radar to measure the respiratory rate and the heartbeat of a subject in challenging indoor scenarios. To simulate a realistic configuration for ambient assisted living (AAL) applications, in which the thorax orientation towards the antenna is typically unknown, four different scenarios were considered. Measurements were performed on five volunteers positioned with the chest, left, back, and right side facing the antenna, respectively. The 5 . 8 radar and the antennas used for the measurements were suitably designed for the considered application. To obtain a low cost and compact system, series-fed arrays were preferred over other antenna topologies. The geometry of the patches was opportunely shaped to reduce the side lobe level (SLL) and increase the bandwidth, thus ensuring good system performances. In all scenarios, the vital signs extracted from the radar signal were compared with the ones collected by a photoplethysmograph and a respiratory belt, used as references. A statistical analysis of the measured data on the different subjects and orientations was performed, showing that the radar was able to measure with high accuracy both the respiratory rate and the heartbeat in all considered configurations.