Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management

The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.

Digital twin for healthy indoor environment: A vision for the post-pandemic era

Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.

Sustainable Urban Freight for Energy-Efficient Smart Cities—Systematic Literature Review

Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment;(2) the Consumer as a Service Provider (CaaSP);(3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities. © 2023 by the authors.

Analysis of the spillover effects between green economy, clean and dirty cryptocurrencies

Cryptocurrencies have been widely used as financial instruments over the past decade. Given the development of the cryptocurrency market and the increasing awareness of greener and more energy-efficient tokens, their connection to the green economy has become a popular topic for understanding economic and policy issues. However, the literature still lacks clear evidence on how cryptocurrencies interact with green economy indicators. Therefore, this study examines the correlations and spillover relationships between green economy indices, five black cryptocurrencies, and five clean cryptocurrencies for the U.S., Euro, and Asian markets. To this end, it applies the novel quantile spillover index approach of Ando et al. (2018) to daily data from November 9, 2017, to April 4, 2022. The empirical results show that the overall linkage is stronger for green economy indices and clean cryptocurrencies than for dirty cryptocurrencies. Moreover, green economy indices show net receiving behavior, while cryptocurrencies' results differ across variables, quantiles, and time. In addition, a notable point for clean cryptocurrencies is 2020, which was the start of the COVID-19 pandemic. The overall spillover effect is very high for all quantiles for the three markets, especially for Asia. This outcome signifies the safe harbor property for diversification purposes of the green economy. The results presented in this study are important for investors, regulators and, policymakers, cryptocurrency founders as they seek to be financially integrated and develop a more sustainable business. © 2023

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

Data-informed building energy management (DiBEM) towards ultra-low energy buildings

To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations. © 2022 Elsevier B.V.

Data-Informed Building Energy Management (DiBEM) towards Ultra-Low Energy Buildings

To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations.

Dual-Hop Direct Diffusion Routing Protocol for Energy-Efficient Wireless Sensor Network

In this COVID-19 pandemic situation, health care is on the priority of every human being. The recent development in the miniaturization of intelligent devices has opened many opportunities and played a crucial role in the healthcare industry. The amalgamation of wireless sensor network and Internet of Things is the best example of wireless body area network. These tiny sensor devices have two essential evaluation parameters named as energy efficiency and stability while performing in a group. This paper focuses on various issues of the healthcare system and their solutions. An energy-efficient routing protocol that can provide sensed data to the collection centre or data hub for further processing and treatment of the patients is proposed. Here, we fixed zones for sending data to zone head using distance aware routing, and then zone head send the aggregated data to the data hub. It is better than the low energy adaptive clustering hierarchy (LEACH) by 42% and distance-based residual energy-efficient protocol (DREEP) by 30% in energy efficiency and stability 58% more by LEACH and 39% by DREEP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Energy-Efficient Monitoring of Potential Side Effects from COVID-19 Vaccines

COVID-19 has affected the world for almost two years causing lots of damages and losses of lives. With the development of sensing technology and digital health, recent research studies suggest to use wearable devices for monitoring COVID-19 symptoms or analyzing people's behaviour change. As COVID-19 vaccines are getting widely available, their side effects have raised public concerns, though have not yet been thoroughly studied due to the short deployment time. As far as we know, this work is the first study to use wearable devices and mobile app to collect physiological data to explore potential side effects to human bodies from COVID-19 vaccinations. We designed and developed a mobile sensing system, which can monitor changes of physiological indicators through wearable devices, collect self-reported data from the users and proposed a green data transmission mechanism which can reduce the communication overheads. Pilot study has been conducted to evaluate the feasibility of our system. Preliminary results show that increased resting heart rate (RHR) and changes on average heart rate (HR) are observed in some participants after COVID-19 vaccinations. This study opens up the opportunity to collect larger amount of data and further investigate potential side effects from COVID-19 vaccinations. © 2022 IEEE.

Energy Audit in an Urban Indian Residential Community-A Case Study

This paper presents an energy audit study conducted for an urban residential community in Mumbai. The consumers are categorized using a k-means clustering algorithm based on their electricity consumption. The energy-efficient appliance selection is undertaken by a benchmarking study based on the appliance energy labeling and star rating initiated by the Bureau of Energy Efficiency(BEE) in India. The study establishes the techno-economic feasibility of energy savings in Indian urban households with an average payback period of 3.3 years. The energy-saving opportunities are selected based on each cluster's capital cost and payback period. Sensitivity analysis of electricity tariff of a region on payback period is undertaken. The covid impact analysis on the residential energy consumption is conducted by comparing energy consumption before and after the covid. The benefits are replicable in most Indian households, especially the urban residential consumers with high consumption in regions with high electricity tariffs. © 2022 IEEE.

Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference

Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8 × 8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy). © 2022 IEEE.

In-Memory 3D NAND Flash Hyperdimensional Computing Engine for Energy-Efficient SARS-CoV-2 Genome Sequencing

Hyperdimensional computing (HDC) is a promising paradigm for large-scale genome sequencing. In this work, we explore the feasibility of the in-memory HDC on 3D NAND Flash for genome sequencing. We investigate the HDC engine with geographical region classification of SARS-CoV-2 genome sequences. The simulation results indicate the robustness of the classification accuracy despite the 3D NAND Flash non-idealities. The system performance is evaluated and it achieves 1.21× improvement on energy efficiency compared to PCM-based HDC engine. The area efficiency is also improved by 3.79×. © 2022 IEEE.

Determination of optimized sleep interval for 10 gigabit-passive optical network using learning intelligence

The overall aim of this project is to investigate the application of a machine learning method in finding the optimized length of asleep time interval (TAS) in a cyclic sleep mechanism (CSM). Since past decade, the implementations of CSM in the optical network unit (ONU) to reduce the energy consumption in 10 gigabit-passive optical network (XG-PON) were extensively researched. However, the newest era sees the emergence of various network traffic with stringent demands that require further improvements on the TAS selection. Since conventional methods utilize complex algorithm, this paper presents the employment of an artificial neural network (ANN) to facilitate ONU to determine the optimized TAS values using learning from past experiences. Prior to simulation, theoretical analysis was done using the M/G/1 queueing system. The ANN was than trained and tested for the XG-PON network for optimal TAS decisions. Results have shown that towards higher network load, a decreasing TAS trend was observed from both methods. A wider TAS range was recorded from the ANN network as compared to the theoretical values. Therefore, these findings will benefit the network operators to have a flexibility measure in determining the optimal TAS values at current network conditions.

Antecedents of Consumer’s Purchase Intention Towards Energy-Efficient Home Appliances: An Agenda of Energy Efficiency in the Post COVID-19 Era

This study focuses on the energy efficiency in the past COVID-19 era and targeted the young population of Pakistan who are facing the critical situation of COVID-19 era and much aware that this situation will badly affect the energy situation when COVID-19 will end and they also aware that energy efficient appliances will be the most valuable products after the COVID-19 era. Data was collected from five major cities of Pakistan and analyzed by applying structure equation modelling through smart-PLS 3.3. Results show that knowledge of eco-labels has significant impact on perceived functional values, green trust and purchase intention of energy efficient home appliances. Results further indicate that consumers social responsibility has significant impact on personal norms and purchase intention of energy efficient home appliances. Moreover, functional value and green trust mediates the relationship of knowledge of eco-labels and purchase intention of energy efficient home appliances. Furthermore, attitude towards energy efficient appliances mediates the relationship between consumers social responsibility and purchase intention but surprisingly no mediating affect of attitude between consumer social responsibility and purchase intention of energy efficient home appliances. This study presents an antecedent model for predicting energy-efficient home appliances based on consumer awareness. This study will help companies for technology innovation and improvements in the efficiency of household appliances are among the key functional values that companies should emphasize, in order to attract consumers to value the surprising energy-saving effects of appliances. Copyright © 2022 Jamil, Dunnan, Awan, Jabeen, Gul, Idrees and Mingguang.

Compliance with Building Energy Code for the Residential Sector in Egyptian Hot-Arid Climate: Potential Impact, Difficulties, and Further Improvements

Building energy codes are considered to be an effective policy tool for energy reduction worldwide. However, their application and effectiveness are still limited in developing countries. In Egypt, the residential sector is promising for energy savings, as most of the existing residential buildings are aged with low thermal performance and non-conformance with energy codes. This study aims to raise the awareness of promoting the Egyptian residential energy codes among construction parties, especially end-users, by quantifying the environmental impacts, in terms of energy savings and thermal comfort enhancement. Moreover, it attempts achieving a nearly zero energy building by integrating several energy-efficient measures with renewable energy sources. Thus, in this study, a typical residential building in Cairo was chosen for simulation. The simulation results revealed that applying energy code instructions for building envelope, lighting enhancement and increases in cooling set-points, from 24 °C to 25 °C, saved 37.85% of annual electrical energy and resulted in a cooling reduction of 50.53%. Furthermore, the photovoltaic system incorporation succeeded in transforming the building into a nearly zero energy building. Concerning thermal comfort, the application of passive energy-efficient measures significantly influences indoor thermal comfort, with a 30% reduction in discomfort hours during the cooling season, which represents the main concern in hot climate regions.

The Development of Energy Efficient Routing Protocol Based on SDN Architecture And Intelligent Technique For HIHC-IoT

Due to the COVID-19 virus infections that have occurred recently, the development of an intelligent healthcare protocol that considers emergent heart cases becomes indispensable. This protocol is based on the method that aims to monitor patients remotely by using Internet of Thing (IoT) devices, which do not select the nodes that are nearby the patient's or in the room to choose as a Clusters Head (CH). So on, the energy consumption of these devices will be reduced, because of their highest importance than the other non-medical ones. Accordingly, this paper proposes a method called High Importance Healthcare-Internet of Things (HIHC-IoT), which is suitable for the emergent healthcare conditions of the patient and the caregiver. Furthermore, WSNs have some issues that reduce system performance, such as resource limits for sensors that may affect power supply, memory, communication capacity, and processing units. In the proposed work, the optimum set of CHs has been selected depending on the residual energy, the distance between the nodes, and the HI nodes. In addition, cloud technology, SDN architecture, and an efficient intelligent algorithm called High Importance-Future Search Algorithm (HI-FSA) have been used. Finally, the compered result of normal protocols with the proposed intelligent protocol, showed an increase in network life by about 40% and about 22% for an optimized routing protocol and increasing the number of packets delivered between nodes. © 2021 IEEE.

Hamming Distance Tolerant Content-Addressable Memory (HD-CAM) for DNA Classification

This paper proposes a novel Hamming distance tolerant content-addressable memory (HD-CAM) for energy-efficient in-memory approximate matching applications. HD-CAM exploits NOR-type based static associative memory bitcells, where we add circuitry to enable approximate search with programmable tolerance. HD-CAM implements approximate search using matchline charge redistribution rather than its rise or fall time, frequently employed in state-of-the-art solutions. HD-CAM was designed in a 65nm 1.2V CMOS technology and evaluated through extensive Monte Carlo simulations. Our analysis shows that HD-CAM supports robust operation under significant process variations and changes in the design parameters, enabling a wide range of mismatch threshold (tolerable Hamming distance) levels and pattern lengths. HD-CAM was functionally evaluated for virus DNA classification, which makes HD-CAM suitable for hardware acceleration of genomic surveillance of viral outbreaks, such as Covid-19 pandemics. Author

Enhanced Energy Savings with Adaptive Watchful Sleep Mode for Next Generation Passive Optical Network

A single watchful sleep mode (WSM) combines the features of both cyclic sleep mode (CSM) and cyclic doze mode (CDM) in a single process by periodically turning ON and OFF the optical receiver (RX) of the optical network terminal (ONT) in a symmetric manner. This results in almost the same energy savings for the ONTs as achieved by the CSM process while significantly reducing the upstream delays. However, in this study we argue that the periodic ON and OFF periods of the ONT RX is not an energy efficient approach, as it reduces the ONT Asleep (AS) state time. Instead, this study proposes an adaptive watchful sleep mode (AWSM) in which the RX ON time of ONT is minimized during ONT Watch state by choosing it according to the length of the traffic queue of the type 1 (T1) traffic class. The performance of AWSM is compared with standard WSM and CSM schemes. The investigation reveals that by minimizing the RX ON time, the AWSM scheme achieves up to 71% average energy saving per ONT at low traffic loads. The comparative study results show that the ONT energy savings achieved by AWSM are 9% higher than the symmetric WSM with almost the same delay and delay variance performance.

MMRL: A Multi-Modal Reinforcement Learning Technique for Energy-efficient Medical IoT Systems

The Internet of Medical Things (IoMT) couples the rapid growth of Internet of things (IoT) technologies with smart health systems, leveraging wireless battery-operated devices for remote health monitoring. Since 2019, a surge in the number of COVID-19 patients has increased rapidly, leading to increased strain on hospital resources and leaving some urgent patients behind. This is substantial cause to transform interactive health treatment into intelligent healthcare using edge computing and artificial intelligence (AI) techniques. However, running sophisticated AI-based edge computing techniques on IoT devices with limited battery is not sustainable. Hence, addressing the trade-off between energy-efficiency and smart AI techniques is imperative to maximize the device's lifetime. This paper proposes a Multi-Modal Reinforcement Learning (MMRL) algorithm that will help maximize the IoT device's lifetime using adaptive data compression, energy-efficient communication, and minimum latency, particularly for emergency events. The results showed a 500% longer battery life than the state-of-the-art algorithms in addition to high adaptability to different conditions. © 2021 IEEE

IoT Based Smart Home-Health Monitoring System Using Dempster-Shafer Evidence Theory for Pandemic Situation

With the advancement of the Internet of Things in our smart environment, smart devices are working without human intervention. So home can be converted to intelligent home automation systems to perform its computation automatically. In a pandemic situation, the majority of people have spent their maximum time at home. So indoor air quality, insider's and outsider's health monitoring has become an important issue. As respiratory diseases are the main concern for pandemics, we have to develop an intelligent home system model to monitor healthy environmental conditions for the users. This paper proposes an energy-efficient smart system model to monitor the health and environmental condition by measuring the carbon monoxide threat level that indirectly affects other atmospheric parameters. Our system alerts when the carbon monoxide level exceeds the safe level. Remote monitoring of the home and health parameters is done in real-time with the help of the system model. For this purpose, we are adopting Dempster-Shafer evidence theory as a mathematical model to aggregate the data coming from different sensors. The sensor nodes track the home and health parameters such as room temperature, humidity, carbon monoxide level, SpO2 level, body temperature, and pulse rate. The smartphone app updates the user's real-time sensor data through the display and indirectly helps to maintain the physical distance. The proposed intelligent home-health system model is compact, cost-effective, energy-efficient for the user, and is especially useful for the quarantined covid affected people in a pandemic situation. © 2022 ACM.