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Abstract Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.

This paper introduces a propagation study at sub-THz frequencies in an urban environment. Deterministic simulations are carried out utilizing a software tool and a high-resolution digital map of the area. Different scenarios and antenna configurations are examined, as well as the relative propagation mechanisms are described. The simulated path loss is found to be well approximated by the close-in (CI) model, delivering path loss exponents equal to 2.1, and 3.4, and shadowing factors 2.5, and 7.2 dB, for line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, respectively. Links that support high data rates are feasible even at NLOS locations but for distances up to 100–150 m. The root-mean-square (rms) delay spread is found in the range of 4.8–44.2 ns and 1.7–38.3 ns, for LOS and NLOS conditions, respectively. The simulated channel exhibits frequency selective characteristics demonstrating coherence bandwidth values between 8.9 and 161.7 MHz. Finally, a multi-cluster model is found to fit well the simulated PDPs. Up to four and two clusters are observed in LOS and NLOS locations, respectively. The cluster and ray decay factors, as well as their intra- and inter-arrivals times are all found to be regulated by the propagation environment and the adopted antenna characteristics.

Rapid advances in antennas, propagation, electromagnetics, and materials are opening new and unexplored opportunities in body area sensing and stimulation. Next-generation wearables and implants are seamlessly providing round-the-clock monitoring. In turn, numerous applications are brought forward with the potential to ultimately transform healthcare, sports, consumer electronics, and beyond. This review paper provides a comprehensive overview, discusses challenges and opportunities, and indicates future directions for: (a) enabling technologies needed to make body area sensing and stimulation a reality, and (b) emerging bioelectromagnetics applications that may readily benefit from such technologies.

After almost three years since the first articles of the IEEE Open Journal of Antennas and Propagation (OJAP) got published, my service as the founding Editor-in-Chief of OJAP comes to completion. I depart with a great sense of satisfaction and gratitude. Guiding the establishment and evolution of OJAP has been a truly rewarding and enjoyable journey that I had the opportunity to share with all of you, the journal’s authors and readers.

The IEEE Open Journal of Antennas and Propagation (IEEE OJAP) is now two years old! Gripped by a blend of satisfaction and excitement, I am skimming the journal’s content as I am writing this editorial, looking back at the previous year and reflecting on the significant growth that OJAP has undergone.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug–drug, food–drug, and supplement–drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.

In this article, an unobtrusive and affordable sensor-based multimodal approach for real time recognition of engagement in serious games (SGs) for health is presented. This approach aims to achieve individualization in SGs that promote self-health management. The feasibility of the proposed approach was investigated by designing and implementing an experimental process focusing on real time recognition of engagement. Twenty-six participants were recruited and engaged in sessions with a SG that promotes food and nutrition literacy. Data were collected during play from a heart rate sensor, a smart chair, and in-game metrics. Perceived engagement, as an approximation to the ground truth, was annotated continuously by participants. An additional group of six participants were recruited for smart chair calibration purposes. The analysis was conducted in two directions, firstly investigating associations between identified sitting postures and perceived engagement, and secondly evaluating the predictive capacity of features extracted from the multitude of sources towards the ground truth. The results demonstrate significant associations and predictive capacity from all investigated sources, with a multimodal feature combination displaying superiority over unimodal features. These results advocate for the feasibility of real time recognition of engagement in adaptive serious games for health by using the presented approach.

It is with great delight and sincere privilege that I am writing this editorial to celebrate the one-year anniversary of the IEEE Open Journal of Antennas and Propagation (IEEE OJAP), the first gold fully open access journal of the IEEE Antennas and Propagation Society (AP-S). Throughout its first year, OJAP has undergone significant growth and pursued multilevel expansion with the ultimate goal of catalysing innovation through accelerated, top-quality scientific publication [item 1) in the Appendix].

The IEEE Open Journal of Antennas and Propagation is live! It is with great pleasure and sincere privilege, as the inaugural Editor-in-Chief, that I am writing this message to introduce the first volume of the IEEE Open Journal of Antennas and Propagation (OJAP), the first gold fully-open access journal of the IEEE Antennas and Propagation Society (AP-S).

Parkinson’s disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments.