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Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an internet of things (IoT) ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This paper aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End-users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energy-saving recommendations are delivered to end-users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors' knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms., 10 pages, 9 figures, 5 tables, IEEE Systems Journal, 2021

Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first investigated, including data collection devices and public data sets. Next, event-based and non-event-based NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations. 2022 Wiley Periodicals LLC. This paper was made possible by National Priorities Research Program (NPRP) Grant No. 10?0130?170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This study offers a comparative analysis of different diagnostic techniques for lung pathologies from an engineering standpoint. The review concentrates on three primary categories of methods: electronic nose-based (E-nose) detection, computer vision or image processing, and biosensors such as Graphene-FET (GFET). The e-nose-based detection technique uses electronic sensors to recognize Volatile Organic Compounds (VOCs) in the exhaled breath. These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The computer vision processing method involves the application of advanced imaging techniques and Machine Learning algorithms to scrutinize and diagnose lung pathologies and Ventilator-Associated Pneumonia (VAP). Lastly, biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological samples. This information can be used to diagnose lung pathologies and VAP. The present paper examines the current state-of-the-art in each methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscores the potential of these techniques to enhance the diagnosis of lung pathologies and VAP. Additionally, it emphasizes the necessity for further research to optimize their performance and clinical usefulness.

The emerging field of flexible bio-mechatronic technologies, such as wearable sensors, actuators and robots, are playing more and more crucial roles in the design and development of next generation healthcare and rehabilitation systems that safely and smartly interact with human patients. To create a complex and intelligent healthcare and rehabilitation system, it is necessary to design, develop and implement advanced wearable sensors, Internet of Things (IoT) and intelligent algorithms. The papers of this special issue discuss various research and development achievements in the field of IoT systems for intelligent healthcare and rehabilitation, including developments, methodologies, and applications to biological sensors, IoT infrastructures, soft robotics, biomechanical modeling, novel algorithms, big data analysis, as well as devices and tools for health monitoring, etc. This special issue provides readers with a comprehensive collection of the state-of-the-art advances in the field of intelligent healthcare and rehabilitation.

Simulation plays an important part in enhancing the outcomes of clinical training worldwide. In particular, extracorporeal membrane oxygenation (ECMO) is a life-saving procedure that utilizes a cardiopulmonary bypass circuit to offer short or mid-term respiratory and circulatory assistance to seriously ill patients. After the current Coronavirus (COVID-19) pandemic hit the world, in-person simulations became quite challenging to organize, due to the precautionary physical distancing practices. Due to ECMO's reliance on hands-on training, there is a need for a physical distancing-compatible solution that does not compromise on learning fidelity. Consequently, an assessment application has been developed to assist ECMO centers in carrying out simulations while being assessed by ECMO educators remotely. In this article, we conceptually introduce a revolutionary tablet-based assessment application for convenient, yet sophisticated and structured assessment of ECMO learners as a companion to an ECMO simulator and instructor application. The proposed application allows real-time assessment of the ECMO simulation experience and creates standardized simulation assessment curricula synced with a high-realism ECMO simulator. Initial app prototype showcases basic functionality, low communication latency, and a prototype simulation system that can simulate various ECMO emergencies. 2008-2011 IEEE. This work was supported by Qatar University under internal Grant M-CTP-CENG-2020-1. Scopus

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible by adopting next-generation energy systems, which leverage artificial intelligence, the Internet of things (IoT), and communication technologies to collect and analyze big data in real-time and effectively run city services. However, training machine learning algorithms to perform various energy-related tasks in sustainable smart cities is a challenging data science task. These algorithms might not perform as expected, take much time in training, or do not have enough input data to generalize well. To that end, transfer learning (TL) has been proposed as a promising solution to alleviate these issues. To the best of the authors’ knowledge, this paper presents the first review of the applicability of TL for energy systems by adopting a well-defined taxonomy of existing TL frameworks. Next, an in-depth analysis is carried out to identify the pros and cons of current techniques and discuss unsolved issues. Moving on, two case studies illustrating the use of TL for (i) energy prediction with mobility data and (ii) load forecasting in sports facilities are presented. Lastly, the paper ends with a discussion of the future directions.

Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end-users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one-class support vector machine, namely UAD-OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD-M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule-based algorithm is introduced to extract the micromoments representing the intent-rich moments, in which the end-users make decisions to consume energy; and (iii) an improved K-nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD-M2 achieves both a highest abnormality detection performance and real-time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real-world data set collected at the Qatar University energy lab. 2021 Wiley Periodicals LLC This paper was made possible by National Priorities Research Program (NPRP) grant no. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library. Scopus

Domestic user behavior is a crucial factor guiding overall power consumption, necessitating the development of systems that analyze and help shape energy-efficient behavior. Therefore, the most important step in the process is the collection and understanding of highly detailed domestic consumption data. This article presents an appliance-based energy data collection and analysis system for energy efficiency applications. It leverages the concept of micro-moments, which are short-timed and energy-based events that form the overall energy behavior of the end user. The system comprises sensing modules for recording energy consumption, occupancy, temperature, humidity, and luminosity storing recordings on a database server. Sensing parameters were tested in terms of connection stability and measurement accuracy. A four-week contextual appliance-level dataset has been collected from research cubicles. Collected data were also classified into corresponding micro-moments with a variety of classifiers including ensemble decision trees and deep learning, achieving high stability and accuracy of 99%. Further, the micro-moment usage efficiency is calculated to quantify the efficiency of usage at the appliance level. 2021 IEEE. Manuscript received November 4, 2019; revised March 23, 2020; accepted April 22, 2020. Date of publication June 9, 2020; date of current version March 9, 2021. This article was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. (Corresponding author: Abdullah Alsalemi.) Abdullah Alsalemi, Yassine Himeur, and Faycal Bensaali are with the Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar (e-mail: a.alsalemi@qu.edu.qa; yassine.himeur@qu.edu.qa; f.bensaali@qu.edu.qa). Scopus

Internet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on the cloud. They collect data from a multitude of sensors, process it in real-time on the cloud and deliver immediate actions to sets of actuators that are installed locally. In this work, we present the (EM)3 project (Consumer Engagement towards Energy Saving Behaviour by Means of Exploiting Micro Moments and Mobile Recommendation Systems), which combines data collection, information abstraction, timed recommendations for energy saving actions and automations that promote energy saving in a household or office setup. The advantage of the (EM)3 project is that each room or office setup is controlled locally on an edge device, thus removing the need to share data to the cloud. The current article details on the data and information processing aspects of the (EM)3 solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. It also demonstrates the scalability of the solution by simulating a simple scenario of distributed data collection and processing on the edge nodes, which takes advantage of federated learning in order to adapt to the needs of multiple users without exposing their privacy. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This paper introduces REHAB-C, a goal-based context-aware recommendation system that supports users in transforming their energy habits. A combination of smart sensors and actuators collects information about user actions and their context and evaluates the actions to be proposed. Based on user decisions, REHAB-C either triggers an energy saving action, postpones or cancels it and records user preferences. The recommendation system continuously evaluates the user context and action against a rule base along with user goals and prioritizes the recommended actions, also considering other user preferences. The prototype implementation of REHAB-C is evaluated on two real-world scenarios, in an office setup. The user actions and presence are recorded for four weeks, as well as the user behavior towards energy saving, and his preferences on the recommended actions. The analysis of recorded results shows that goal-based context-aware recommendations can help users to improve their energy efficiency and in the long term can automatically adapt to user habits and reduce their energy footprint. 2020 Elsevier B.V. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRonPlateau. A dataset, published in 2018 and produced by the Medical Segmentation Decathlon Challenge organizers, called Task 8 Hepatic Vessel (MSDC-T8) has been used for testing and validation. The reported results that have the same number of maximum epochs (75), and are the average of 5-fold cross-validation, indicate that ReduceLRonPlateau converges faster while maintaining a similar or even better loss score on the validation set when compared to OneCycleLR. The epoch at which the peak LR occurs perhaps should be made early for the OneCycleLR such that the super-convergence feature can be observed. Moreover, the overall results outperform the state-of-the-art results from the researchers who published the liver masks for this dataset. To conclude, both schedulers are suitable for medical segmentation challenges, especially the MSDC-T8 dataset, and can be used confidently in rapidly converging the validation loss with a minimal number of epochs., Comment: 8 pages, 4 figures, 1 table, currently submitted The 2nd International Conference on Intelligent Systems and Patterns Recognition (ISPR'2022)

Edge computing is attracting an increasing attention presently even though most of the building energy efficiency solutions are still using cloud computing for gathering, pre-processing and analyzing energy data. However, edge computing still requires more power in order to be used alone to meet the high computation demand of artificial intelligence based energy saving solutions. Meanwhile, a hybrid edge-cloud architecture can be the best current approach to implement energy efficiency systems. It provides end-users and utility companies with a flexible control of their energy usage footprints, minimizes the cost of cloud hosting, and improves privacy-preservation. Accordingly, in this paper, we present a novel energy efficiency system based on a hybrid edge-cloud computing architecture. To analyze energy and occupancy data collected through different smart meters and occupancy sensors, we use a micro-moment approach to cluster energy observations into different categories representing both normal and abnormal energy usage. Following, a deep micro-moments (deepM2) scheme is deployed to automate the Anomaly Detection task, where a new approach called deepM2-AD is developed. Moving forward, deepM2-AD is implemented on three different architectures, defined as edge-only, cloud-only and hybrid edge-cloud to evaluate their performance and identify their merits and demerits. Overall, the hybrid edge-cloud architecture has presented the best compromise in terms of improving the processing speed, curtailing the cost of cloud hosting, and reducing the communication latency. Therefore, it has a great potential for supporting real-time energy consumption anomaly detection applications that help in minimizing wasted energy. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Acknowledgments. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings' management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings' performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings. 2022, The Author(s). This publication was made possible by NPRP Grant No. NPRP12S-0222-190128 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors. Scopus

Observing the electricity consumption nowadays may be extremely daunting; therefore, optimizing the consumers' usage is critical to ensuring the sustainability of energy resources. The employment of innovative technologies (e.g., artificial intelligence (AI) and Internet of things (IoT)) to boost energy efficiency in houses or buildings is becoming more and more vital to support the sustainability and preservation 234of resources. This chapter sheds light on the application of AI and IoT in improving residential energy efficiency by advancing state-of-the-art, evidence-based, AI-enabled energy efficiency recommendation systems (RSs). The framework makes use of AI and micro-moments concept and utilizes a variety of IoT sensors strategically positioned throughout the house to transform any habit according to the habit loop, where the end-user has to go through a cue, a routine and a reward. A RS is adopted in this change cycle since it relates the action to the reward and, hence, reinforces the new routine, where notifications are sent to users via the Home-Assistant 1 app with the energy-saving advice. In order to evaluate our energy-saving framework, a mini-pilot is initiated with a number of users with distinct electrical perspectives (up to 10 users) in various locations. The study's findings determine the impact of AI, IoT and RSs on energy efficiency. 2023 Anshul Verma, Pradeepika Verma, Yousef Farhaoui and Zhihan Lv. Scopus

Background: Training is an essential aspect of providing high-quality treatment and ensuring patient safety in any medical practice. Because extracorporeal membrane oxygenation (ECMO) is a complicated operation with various elements, variables, and irregular situations, doctors must be experienced and knowledgeable about all conventional protocols and emergency procedures. The conventional simulation approach has a number of limitations. The approach is intrinsically costly since it relies on disposable medical equipment (i.e., oxygenators, heat exchangers, and pumps) that must be replaced regularly due to the damage caused by the liquid used to simulate blood. The oxygenator, which oxygenates the blood through a tailored membrane in ECMO, acts as a replacement for the patient's natural lung. For the context of simulation-based training (SBT) oxygenators are often expensive and cannot be recycled owing to contamination issues. Methods: Consequently, it is advised that the training process include a simulated version of oxygenators to optimize reusability and decrease training expenses. Toward this goal, this article demonstrates a mock oxygenator for ECMO SBT, designed to precisely replicate the real machine structure and operation. Results: The initial model was reproduced using 3D modeling and printing. Additionally, the mock oxygenator could mimic frequent events such as pump noise and clotting. Furthermore, the oxygenator is integrated with the modular ECMO simulator using cloud-based communication technology that goes in hand with the internet of things technology to provide remote control via an instructor tablet application. Conclusions: The final 3D modeled oxygenator body was tested and integrated with the other simulation modules at Hamad Medical Corporation with several participants to evaluate the effectiveness of the training session. 2022 The Authors. Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC. This article was supported by the Qatar University Internal Grant No. M-CTP-CENG-2020-1. The findings achieved herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library. Scopus

The detection of anomalous energy usage could help significantly in signaling energy wastage and identifying faulty appliances, especially if the individual power traces are analyzed. To that end, this paper proposes a novel abnormal energy consumption detection approach at the appliance-level using autoencoder and micro-moments. Accordingly, energy usage footprints of different household appliances along with occupancy patterns are analyzed for modeling normal energy consumption behaviors, and on the flip side, detecting abnormal usage. In effect, energy micro-moments occur when end-users reflexively (i) switch on/off an appliance to start/stop an energy consumption action; (ii) increase/reduce energy consumption of a specific appliance; and (iii) enter/leave a specific room. Put differently, energy micro-moments are captured by reference to end-users' daily tasks usually performed to meet their preferences. In this regard, energy micro-moment patterns are extracted from appliance-level consumption fingerprints and occupancy data using an innovative rule-based algorithm to represent the key intent-driven moments of daily energy use, and hence model normal and abnormal behaviors. Moving forward, energy micro-moment patterns are fed into an autoencoder including 48 input/output neurons, and 4 neurons in the intermediate layer aiming at reducing the computational cost and improving the detection performance. This has helped in accurately detecting two kinds of anomalous energy consumption, i.e. "excessive consumption" and "consumption while outside", where up to 0.95 accuracy and F1 score have been achieved, for example, when analyzing microwave energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Acknowledgements. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research., 25 pages, 6 figures, 3 tables

When investigating how people conserve energy, most researchers and decision-makers render a conceptual distinction between prevention (e.g. unplugging devices) and productivity measures. Nevertheless, such a two-dimensional approach is inefficient from both a conceptual and policy standpoint, since it ignores individual differences that influence energy-saving behavior. Preserving electricity in homes and buildings is a big concern, owing to a scarcity of energy resources and the escalation of current environmental issues. Furthermore, the COVID-19 social distancing policies have resulted in a temporary transition of energy demand from industrial and urban centers to residential areas, resulting in greater consumption and higher costs. In order to promote the sustainability and preservation of resources, the use of new technologies to increase energy efficiency in homes or buildings becomes increasingly necessary. Hence, the goal of the project is to provide consumers with evidence-based data on the costs and advantages of ICT-enabled energy conservation approaches, as well as clear, timely, and engaging information and assistance on how to realize the energy savings that are attainable, in order to boost user uptake and effectiveness of such techniques. End-users can visualize their consumption patterns as well as ambient environmental data using the Home-assistant user interface. More notably, explainable energy-saving recommendations are delivered to end-users in form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors’ knowledge, this is the first attempt for developing and implementing an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms.

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions' responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic. 2022 The Author(s) This paper is made possible by National Priorities Research Program, Qatar (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library. Scopus

Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HMC) in Qatar, there is a critical need of expert ECMO staff. Thus, a modular ECMO simulator is being developed to enhance the training process in a cost-effective manner. This ECMO simulator gives the instructor the ability to control the simulation modules and run common simulation scenarios through a tablet application. The core modules of the simulation system are placed in the patient unit. The unit is designed modularly such that more modules can be added throughout the simulation sessions to increase the realism of the simulation sessions. The new approach is to enclose the patient unit in a trolley, which is custom-designed and made to include all the components in a modular fashion. Each module is enclosed in a separate box and then mounted to the main blood simulation loop box using screws, quick connect/disconnect liquid fittings, and electrical plugs. This method allows fast upgrade and maintenance for each module separately as well as upgrading modules easily without modifying the trolley’s design. The prototype patient unit has been developed for portability, maintenance, and extensibility. After implementation and testing, the prototype has proven to successfully simulate the main visual and audio cues of the real emergency scenarios, while keeping costs to a minimum Qatar University Scopus

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes. The Author(s) 2021. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to acknowledge Qatar National Research Fund project EMBIoT (Proj. No. NPRP 9-114-2-055) project, under the auspices of which the work presented in this paper has been carried out. Scopus

Extracorporeal membrane oxygenation (ECMO) is a modified form of CPB that supports intensive care patients' vital functions during recovery from cardiac or pulmonary trauma. ECMO, although lifesaving, is vulnerable to a plethora of mechanical complications which can cause mortality. This is why developing advanced training systems is of crucial importance. In this paper, as part of an ECMO simulator for training management, a novel thermochromic heater-cooler system is presented. The need of such contribution arises from the lack of high-realism blood simulation methodologies Hence, developed upon thermochromic ink, cost-effective blood simulation is achieved by temperature adjustment, simulating oxygenation and hypoxemia. The system has been developed as a prototype with successful and reversible transitions between dark and bright red blood color. After addressing the limitations, the heater-cooler will be integrated with the ECMO simulator, allowing unpreceded cost-efficient simulation possibilities. Qatar University Scopus

Internet of Energy (IoE) is revolutionizing the building energy industry by introducing numerous innovations that help in data collection, interpretation, and behavioural improvement. Consequently, collecting and analyzing big data is fiercely impacting every field and research area in the context of energy utilization, and therefore, the concept of micro-moments is a timed encapsulation of the user's interaction with the building's appliances. In this article, a high-performance yet cost-efficient edge-based IoE platform for Energy Efficiency in buildings, called Edge-IoE3 is presented, which allows sensing, processing and analyzing energy data in residential buildings. The proposed platform, in the form of a smart plug, consists of two units: a data collection unit and a data processing unit. The data collection unit includes multiple sensors that measure energy consumption along with temperature, humidity, luminosity, and room occupancy. Innovative micro-moment analysis based on ensemble bagging trees is performed to extract pertinent energy consumption micro-moments, which aid in improving consumption behavior with recommender systems. Obtained results have shown a classification accuracy up to 99% of the sensed data and a computation time of 0.1 sec. A number of case studies is presented as guidance on the applications of the proposed solution, and as a building block for improving energy efficiency in buildings. 2001-2012 IEEE. This work was supported by the National Priorities Research Program (NPRP) through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 10-0130-170288. Scopus

Introduction: Almost two million people worldwide die annually due to hepatic-related diseases. Half of these diseases are attributed to cirrhosis and the other half are related to hepatitis and hepatocellular carcinoma (HCC). The liver is also a metastasis hub from adjacent organs. This research aims to create an accurate high-quality delineation of the human liver and prepare them to be 3D printed for medical analysis to help aid medical practitioners in pre-procedural planning. Materials and Methods: Convolutional neural networks (ConvNets) are used to perform the liver tissues delineation. A famous ConvNet, named U-net, is used as the basis benchmark architecture that is also known for its great outcomes in the medical segmentation field. Contrast-enhanced computerized tomography (CT) scans are used from the famous Medical Segmentation Decathlon Challenge (Task 8: Hepatic Vessel), abbreviated as MSDC-T8. It contains 443 CT scans, which is considered the largest dataset that contains both the tumors and vessels ground-truth segmentation. Some researchers also generated the liver masks for this dataset, making it a complete dataset that contains all the relevant tissues’ ground-truth masks. Results: Currently, the liver delineation has been successfully done with very high DICE = 98.12% (higher than the state-of-the-art results DICE = 97.61%), where a comparison between two famous schedulers namely, ReduceLRonPlateau and OneCycleLR has been conducted. Moreover, the 3D liver volume creation has also been completed and built via the marching cube algorithm. Conclusions/Future Directions: The developed ConvNet can segment livers with high confidence. The tumor(s) and vessels tissues segmentation are also under investigation now. Moreover, newly devised self-organized neural networks (Self-ONN) look promising and will be investigated soon. Lastly, a GUI will be built so that the medical practitioner can just insert the CT volume and get the 3D liver volume with all the segmented tissues.

Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower the chances of survival for the elderly, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that includes a sensing device transmitting data to a mobile application through a cloud-connected gateway device. Then, the focus is shifted to the algorithmic aspect where multiple features are extracted from 3-axis accelerometer data taken from existing datasets. The results emphasize on the significance of Continuous Wavelet Transform (CWT) as an influential feature for determining falls. CWT, Signal Energy (SE), Signal Magnitude Area (SMA), and Signal Vector Magnitude (SVM) features have shown promising classification results using K-Nearest Neighbors (KNN) and E-Nearest Neighbors (ENN). For all performance metrics (accuracy, recall, precision, specificity, and F1 Score), the achieved results are higher than 95% for a dataset of small size, while more than 98.47% score is achieved in the aforementioned criteria over the UniMiB-SHAR dataset by the same algorithms, where the classification time for a single test record is extremely efficient and is real-time, 30 Pages, 9 figures, 9 tables. This is a the Accepted Manuscript version of the article published in Biomedical Signal Processing and Control (URL: https://doi.org/10.1016/j.bspc.2021.102532)

Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transforming human behavior rather than device metrics and benchmarks. In this paper, a micro-moment-based smart plug system is proposed as part of a larger multi-appliance energy efficiency program. The smart plug, which includes two sub-units: the power consumption unit and environmental monitoring unit collect energy consumption of appliances along with contextual information, such as temperature, humidity, luminosity and room occupancy respectively. The plug also allows home automation capability. With the accompanying mobile application, end-users can visualize energy consumption data along with ambient environmental information. Current implementation results show that the proposed system delivers cost-effective deployment while maintaining adequate computation and wireless performance., Comment: This paper has been accepted in SCA2020: The Fifth international conference on Smart City Applications