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Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (<sc>ICQ</sc>) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula>, e.g., a person confirmed to be a virus carrier, an <sc>ICQ</sc> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc>ICQ</sc>, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc>ICQ</sc>. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals. IEEE
ABSTRACT
In recent years, the novel corona virus pandemic is raging around the world, and the safety of home environment and public environment has become the focus of people's attention [2]. Therefore, the research on disinfection robot has become one of the important directions in the field of machinery and artificial intelligence. This paper proposes a robot with the STM32 MCU as the core of disinfection, and is equipped with a variety of sensors and a camera vision, has the original cloud service management platform, the remote deployment of navigation, based on visual SLAM to realize high precision navigation and positioning, can realize to indoor environment autonomously route planning, automatic obstacle avoidance checking, disinfection, epidemic prevention function, at the same time can pass Bit computer software realizes remote control of robot, which has great development potential. © 2022 ACM.
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Quarantine is the process of restricting and separating the movement of people who have been exposed to a contagious disease to avoid proliferation. Quarantined subjects were monitored manually, and patients tended to abscond or 'run away.' In the Philippines, there was a lack of research and the absence of similar technology commercially available related to this matter. Project Bantay integrated Artificial Intelligence of Things (AIoT), indoor positioning systems, and wearable technology to alleviate the shortage of personnel, long-Term savings on workforce utilization in the government, and predict absconding or 'run-Away' potentially infectious individuals in our current and future quarantine facilities. Also, it included information system development for monitoring quarantined subjects' heart rates and temperatures that would eventually help the government combat and prepare for a similar unexpected pandemic. The prototype would start by turning on the device after being placed on the quarantined subject. The device must be linked to the router, and the sensors must then be calibrated. The web interface should receive and be able to see data readings, including temperature, heart rate, the location of the subject being quarantined, and the removal of the device via an IR reading. The temperature, heart rate sensor, and indoor positioning all measured above a p-value of 0.05, which accepted the null hypothesis, confirming that the actual and commercial product versus Project Bantay's temperature, heart rate, and indoor positioning accuracy was statistically the same. The anti-Absconding tendencies of the hypothetical dataset using machine learning data analytics showed that among four inherently multioutput machine learning regression algorithms, the Decision Tree Regression Algorithm could output a much better result in determining the tendencies of a subject to abscond from the quarantine facility. © 2022 IEEE.
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The COVID-19 pandemic shows growing demand of robots to replace humans for conducting multiple tasks including logistics, patient care, and disinfection in contaminated areas. In this paper, a new autonomous disinfection robot is proposed based on aerosolized hydrogen peroxide disinfection method. Its unique feature lies in that the autonomous navigation is planned by developing an atomization disinfection model and a target detection algorithm, which enables cost-effective, point-of-care, and full-coverage disinfection of the air and surface in indoor environment. A prototype robot has been fabricated for experimental study. The effectiveness of the proposed concept design for automated indoor environmental disinfection has been verified with air and surface quality monitoring provided by a qualified third-party testing agency. Note to Practitioners - Robots are desirable to reduce the risk of human infection of highly contagious virus. For such purpose, a novel autonomous disinfection robot is designed herein for automated disinfection of air and surface in indoor environment. The robot structure consists of a mobile carrier platform and an atomizer disinfection module. The disinfection modeling is conducted by using the measurement data provided by a custom-built PM sensor array. To achieve cost-effective and qualified disinfection, a full-coverage path planning scheme is proposed based on the established disinfection model. Moreover, for specifically disinfecting the frequently contacted objects (e.g., tables and chairs in offices and hospitals), a target perception algorithm is proposed to mark the localization of these objects in the map, which are disinfected by the robot more carefully in these marked areas. Experimental results indicate that the developed disinfection robot offers great effectiveness to fight against the COVID-19 pandemic. © 2004-2012 IEEE.
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The countermeasure for preventing COVID-19 should be further studied in order to make sure countries are prepared for the endemic phase. The biggest challenge of COVID-19 is its high infection rate and infection mortality rate. Robots offer a very good solution to this, hence, we developed a robot that can autonomously navigate a closed indoor room, sanitize it, and monitor social proximity practices. The quality of the hardware design, electronic system and software developments are conducted and experimental works to test the performance of the robot are performed. © 2022 IEEE.
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Contact tracing is a key approach to control the spread of Covid-19 and any other pandemia. Recent attempts have followed either traditional ways of tracing (e.g. patient interviews) or unreliable app-based localization solutions. The latter has raised both privacy concerns and low precision in the contact inference. In this work, we present the idea of contact tracing through the multipath profile similarity. At first, we collect Channel State Information (CSI) traces from mobile devices, and then we estimate the multipath profile. We then show that positions that are close obtain similar multipath profiles, and only this information is shared outside the local network. This result can be applied for deploying a privacy-preserving contact tracing system for healthcare authorities. © 2022 Owner/Author.
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Currently, the most effective way to reduce transmission of COVID-19 is to differentiate between close contacts. Location points of close contact are essential for differentiation. As a major mode of transportation, ships provide a vehicle for virus transmission. Timely detection location of close contacts inside a ship can prevent the spread of viruses. Location-based services can be provided for ship passengers. Bluetooth is widely available in many wearable devices. The Bluetooth 5.1 angle of arrival (AoA) indoor positioning algorithms can provide a certain indoor positioning accuracy for ship passengers. The two most essential parameters in Bluetooth 5.1 AoA indoor positioning are elevation angle and azimuth angle. Elevation and azimuth are often not accurate enough due to noise, which increases indoor positioning errors. As a result, this paper proposes a mean optimization filter for ship environments, which combines the box plot method to improve Bluetooth 5.1 AoA indoor positioning accuracy, with an RMSE of 0.34 m. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
The indoor positioning system (IPS) has a wide range of applications, due to the advantages it has over Global Positioning Systems (GPS) in indoor environments. Due to the biosecurity measures established by the World Health Organization (WHO), where the social distancing is provided, being stricter in indoor environments. This work proposes the design of a positioning system based on trilateration. The main objective is to predict the positioning in both the 'x' and 'y' axis in an area of 8 square meters. For this purpose, 3 Access Points (AP) and a Mobile Device (DM), which works as a raster, have been used. The Received Signal Strength Indication (RSSI) values measured at each AP are the variables used in regression algorithms that predict the x and y position. In this work, 24 regression algorithms have been evaluated, of which the lowest errors obtained are 70.322 [cm] and 30.1508 [cm], for the x and y axes, respectively. © 2022 IEEE.
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Due to the prevalence of COVID-19, providing safe environments and avoiding exposure to the virus play a pivotable role in our daily lives. As a well-established measurement, contact tracing is widely applied to suppress its spread. Most of the digital contact tracing systems merely detect direct face-to-face contact based on estimated proximity and do not quantify the exposed virus concentration. Indirect environmental exposure due to virus survival time in the air and constant airborne transmission is rarely considered quantitatively. In this work, to provide accurate awareness of the virus quanta concentration in different origins at various times, we propose iSTCA, a self-containing contact awareness approach with spatiotemporal information considered explicitly. Smartphone-based PDR is employed to precisely achieve the location and trajectories for distance estimation and time induction without extra infrastructure involved, in which the accumulative error is calibrated by recognized landmarks in space. A custom deep learning model composed of CNN and LSTM for both the local correlation and long-term dependency extraction is utilized to identify landmarks. By the integration of spatial distance and time difference, the virus quanta concentration of the entire indoor environment is quantitatively calculated at any time with all contributed virus particles. We conduct an experiment based on practical scenario to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.8 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment. © 2022 ACM.
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COVID-19 digital contact tracing applications for smartphones have become popular worldwide to reduce the effects of the pandemic. We considered that contact information between smartphones used in these applications can be used for the indoor localization of pedestrians. In this paper, we propose two indoor pedestrian localization methods based on contact information obtained from Bluetooth low energy (BLE) beacons installed in pedestrian's smartphones. Proposed method 1 is multilateration, and proposed method 2 solves a nonlinear optimization problem to further improve the accuracy of method 1. These two proposed methods comprise three steps: (1) the smartphones and anchor nodes recognize the proximity relationship with neighbor nodes using BLE signals transmitted from other smartphones and anchor nodes. The recognized proximity relationship is sent to a server. (2) The server estimates the distance between each node (smartphone or anchor node) from the proximity relationship. (3) The positions of smartphones are estimated based on the distance between nodes estimated by the server. We verified the localization accuracy of the proposed methods through simulation experiments. In an indoor area of 15 m × 30 m, the average localization error of the proposed method 2 was 0.74 m when the pedestrian density was 0.5 /m2. © 2022 IEEE.
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Several systems deal with human mobility. Most of them are for outdoor environments and use mobile phones to capture data. However, there is a growing interest of enterprises to consider indoor movement to take employees and client classes into account. Moreover, they usually want to assign semantics to the visited locations. We propose a visual exploration tool for analyzing the dynamics of individual movements in an indoor environment in this work. We present the use of suitable charts and animations to explore these complex data better. Finally, we argue that one could use our solution to monitor social distancing in indoor environments, which is a sensible thing during the current COVID-19 pandemic. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
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The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preserving is an essential application towards containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different teleco providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this paper, we propose CellTrace, a deep learning-based social distance preserving system. Specifically, CellTrace finds a cross-provider representation using a deep learning version of Canonical Correlation Analysis. Different providers’data are highly correlated in this representation and used to train a localization model for estimating the social distances. Additionally, CellTrace incorporates different modules that improve the deep model’s generalization against overtraining and noise. We have implemented and evaluated CellTrace in two different environments with a side-by-side comparison with the state-of-the-art cellular localization and contact tracing techniques. The results show that CellTrace can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a sub-meter median error and 97% accuracy, respectively. In addition, we show that CellTrace has robust performance in various challenging scenarios. Author
ABSTRACT
Location fingerprinting based on Received Signal Strength Indicator (RSSI) has become a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. The use of Artificial Intelligence (AI)/Machine Learning (ML) technologies like Deep Neural Networks (DNNs) makes location fingerprinting more accurate and reliable, especially for large-scale multi-building and multi-floor indoor localization. The application of DNNs for indoor localization, however, depends on a large amount of preprocessed and deliberately-labeled data for their training. Considering the difficulty of the data collection in an indoor environment, especially under the current epidemic situation of COVID-19, we investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building;unlike Single-Output Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI observations from multiple Access Points (APs) deployed closely to each other (e.g., APs on the same floor of a building) by collectively handling them. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments using a recently-published work based on Recurrent Neural Network (RNN) indoor localization model and the UJIIndoorLoc, i.e., the most popular publicly-available multi-building and multi-floor indoor localization database;the RNN model trained with the UJIIndoorLoc database, augmented by using the whole RSSI data of a building in fitting an MOGP model (i.e., by a single building), outperforms the other two augmentation methods and reduces the mean three-dimensional positioning error from 8.62 m to 8.42 m in comparison to the RNN model trained with the original UJIIndoorLoc database. © 2022 IEEE.
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Since the 11th of March 2020 when the World Health Organization declared the novel COVID-19 outbreak a global pandemic, it registered officially over 5 million deaths worldwide. According to the course of the pandemic, governments encouraged best practices and then ruled out temporary restrictions on daily lives. In this scenario, non-essential labor-intensive sectors were forced to put on hold operations producing massive temporary layoffs. In gradually restoring the economic activities, governments passed several laws to passively mitigate the pathogen transmission in indoor working environments. However, several COVID19-related injuries were filled by manufacturing companies. According to the outlined conditions, this paper proposes an original and advanced hardware and software architecture to prevent the COVID19 transmission in indoor production environments. The aim is to increase the safety of whichever indoor productive workplace through a contact tracing approach. Indoor positioning systems due to their ability to accurately track the movement of tagged entities compose the hardware part. For this purpose, human operatives are equipped with adequate wearable sensors. Raw data acquired are properly mined through advanced algorithms to quantitatively assess the degree of safety of any working setting. Indeed, having as a reference the epidemiological evidence the software part defines an innovative risk index along two correlated dimensions. While the first defines the risk of any worker getting infected during the shift, the other one expresses the degree of COVID19-safety of the shop floor defined by the displacements of the anchors. Benefitting from these targeted and quantitative hints, plant supervisors may redesign the production settings to lower the chances of COVID19 infection. This innovative digital framework is validated in a real case study in the North of Italy which performs manual mechanical processing for the automotive industry.
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The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since most of people's daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT-enabled technologies in the health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated. © 2022
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Artificial intelligence (AI) has entered tourism and become a new service in a tour guide. AI technology can help tourism by providing customized services and attracting visitors to fight with the crisis of the COVID-19 epidemic. This paper introduces how AI tour guide services contribute to tourism and its main issues. The future development of AI tour guides also was discussed at the end and the authors believe lifelong machine learning is the key to developing AI tour guides. © 2021 IEEE.
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A typical indoor localization system relies on the availability of infrastructure such as Wi-Fi Access Points, blue-tooth beacons or antenna arrays. This increases the overall system cost and it may not be feasible for deployment in real environments such as shopping malls. A practical indoor localization system should be one that can function with mini-mum existing infrastructure. The proposed system in this paper leverages on the embedded sensors in off-the-shelf Internet of Things (IoT) devices such as smartphone in conjunction with Quick Response (QR) codes which are widely deployed under the authorities requirement due to COVID-19 pandemic. Our proposed stationary inertial measurement unit (IMU) feature is implemented through a first order finite impulse response (FIR) filter that works along with the QR codes. It has successfully reduced the drift errors suffered by IMU. The performance was evaluated in the testing environment at an university campus. From the evaluation results, the proposed method outperformed the conventional method (IMU only) and hybrid model (IMU + QR code) by 94.9% and 57.7% respectively, making the proposed method a promising technique that can be readily applied to other indoor environments. © 2021 IEEE.
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This paper presents a design of an autonomous mobile robot system, primarily focuses on hospital environments, which focuses on disinfecting the hospital's isolation ward. This paper also primarily concentrates on making this system cost efficient with minimalistic sensors. This system consists of an advance autonomous indoor navigating mobile robot and a ground station, LIDAR, an UVC disinfectant lamp, encoder motors, IMU, etc. and uses ROS to navigate in indoor autonomously, the hospital environment is a busy environment so this paper equally concentrates on dynamically environment navigation, path planning based on global planner algorithms, localisation and navigation is done using particle filter, dwa_local_planner, navfn. The autonomous mobile robot system has been tested and simulated whose results are reliable and efficient. © 2021 IEEE.
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Facing the enduring COVID-19 pandemic, Internet of Things and mobile robots have played an important role in the control of the spread of coronavirus in enterprises, campuses, and in the transportation of Fangcang hospitals. However, mainstream mobile robots still have unsatisfactory performance in indoor and outdoor positioning and navigation in terms of the processing environment, overburdened computing power, and insufficient positioning accuracy. This paper proposes a cloud computing framework based on a quadruped robot for indoor and outdoor hybrid positioning. The purpose is to broaden the application of mobile robots in Internet of Things, such as operating in complex terrain with stairs, slopes, indoor, outdoor, and etc. © 2021 IEEE.
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Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes;however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.