Real-time Location System Research Articles

A Real-Time UWB Location and Tracking System Based on TWR-TDOA Estimation and a Simplified MPGA Layout Optimization

This article designs and implements a 3D moving target positioning and tracking system by using ultrawideband (UWB) technology. The result of the two-way ranging by the time difference of arrival (TWR-TDOA) positioning algorithm is adopted to result in a new resolution that is to resolve the hyperbolic equations. The proposed algorithm is applied to outdoor and indoor scenarios. To minimize the effect of the sensor layout, this article proposes a simplified multi-population genetic algorithm (MPGA) to obtain the optimum distribution of anchors, which can rapidly reduce the number of search iterations. To resolve the low stability of the TDOA algorithm in outdoor and indoor scenarios, the Kalman filter algorithm is utilized to improve the stability and positioning accuracy of this system and a good simulation effect is achieved. The test results show that the system’s positioning error is far less than that of using other methods. The whole system has a feature of high precision, high stability, low complexity, and low cost.

Real-Time Localization and Mapping Utilizing Multi-Sensor Fusion and Visual–IMU–Wheel Odometry for Agricultural Robots in Unstructured, Dynamic and GPS-Denied Greenhouse Environments

Autonomous navigation in greenhouses requires agricultural robots to localize and generate a globally consistent map of surroundings in real-time. However, accurate and robust localization and mapping are still challenging for agricultural robots due to the unstructured, dynamic and GPS-denied environmental conditions. In this study, a state-of-the-art real-time localization and mapping system was presented to achieve precise pose estimation and dense three-dimensional (3D) point cloud mapping in complex greenhouses by utilizing multi-sensor fusion and Visual–IMU–Wheel odometry. In this method, measurements from wheel odometry, an inertial measurement unit (IMU) and a tightly coupled visual–inertial odometry (VIO) are integrated into a loosely coupled framework based on the Extended Kalman Filter (EKF) to obtain a more accurate state estimation of the robot. In the multi-sensor fusion algorithm, the pose estimations from the wheel odometry and IMU are treated as predictions and the localization results from VIO are used as observations to update the state vector. Simultaneously, the dense 3D map of the greenhouse is reconstructed in real-time by employing the modified ORB-SLAM2. The performance of the proposed system was evaluated in modern standard solar greenhouses with harsh environmental conditions. Taking advantage of measurements from individual sensors, our method is robust enough to cope with various challenges, as shown by extensive experiments conducted in the greenhouses and outdoor campus environment. Additionally, the results show that our proposed framework can improve the localization accuracy of the visual–inertial odometry, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in autonomous navigation of agricultural robots.

The Role of GNSS-RTN in Transportation Applications

The Global Navigation Satellite System—Real-Time Network (GNSS-RTN) is a satellite-based positioning system using a network of ground receivers (also called continuously operating reference stations (CORSs)) and a central processing center that provides highly accurate location services to the users in real-time over a broader geographic region. Such systems can provide geospatial location data with centimeter-level accuracy anywhere within the network. Geospatial location services are not only used in measuring ground distances and mapping topography; they have also become vital in many other fields such as aerospace, aviation, natural disaster management, and agriculture, to name but a few. The innovative and multi-disciplinary applications of geospatial data drive technological advancement towards precise and accurate location services available in real-time. Although GNSS-RTN technology is currently utilized in a few industries such as precision farming, construction industry, and land surveying, the implications of precise real-time location services would be far-reaching and more critical to many advanced transportation applications. The GNSS-RTN technology is promising in meeting the needs of automation in most advanced transportation applications. This article presents an overview of the GNSS-RTN technology, its current applications in transportation-related fields, and a perspective on the future use of this technology in advanced transportation applications.

Image Frame Rate Limited Sub-Centimeter Localization System for Hybrid Radio-Optical Wireless Communications

Infrared optical wireless communication (IR-OWC) has received tremendous attention for the high-speed oriented applications. In consideration of issues like power budget and privacy, narrow-width and directional optical beams are commonly applied for IR-OWC. As only users located within specific areas can get the spatially confined optical signals, accurate localization (sub-)systems are required for the central controller to determine the beam-steering strategies. Image-based localization technologies have shown great accuracy performance. However, their localization speed is typically limited by the image transportation delays and processing time. Systematic studies for the phenomena and mechanisms behind the time relevant issues are desired but still rarely reported. Many real-time localization systems and complexity-reduced computation schemes have been published. However, most of the reported systems are performed under rather controlled (ideal) environmental conditions, and issues like deployments and data transportation are commonly neglected. In this paper, a real-time localization testbed for hybrid radio and optical wireless communications is studied. Novel two-stage processes combining quasi-synchronization and local user tracking processing are proposed to increase the localization speed. Comprehensive discussions for the time relevant issues are performed. The detected images are transported through WiFi; hence the localization systems can be flexibly deployed and be easily integrated with the IoT networks. The testbed is implemented under complex environmental conditions to more accurately reveal the practical performance. An overall localization delay of 39.3 ms, which is close to the camera frame rate limitation (29.2 ms), with 0.11 cm localization accuracy is demonstrated for a 1-cm-aperture user device (UD) 180 cm away from the camera.

Ultra Wideband Real-Time Locating System for Tracking People and Devices in the Operating Room

Article Ultra Wideband Real-Time Locating System for Tracking People and Devices in the Operating Room was published on July 1, 2022 in the journal Current Directions in Biomedical Engineering (volume 8, issue 1).

Measuring impact of a quality improvement initiative on glaucoma clinic flow using an automated real-time locating system

BackgroundLean methodology helps maximize value by reducing waste, first by defining what value and waste are in a system. In ophthalmology clinics, value is determined by the number of patients flowing through the clinic for a given time. We aimed to increase value using a lean-methodology guided policy change, then assessed its impact on clinic flow using an automated radiofrequency identification (RFID) based real-time locating system (RTLS).MethodsA total of 6813 clinical visits occurred at a single academic institution’s outpatient glaucoma clinic between January 5, 2018 to July 3, 2018. Over that period, 1589 patients comprising 1972 (29%) of visits were enrolled, with 1031 clinical visits occurring before and 941 visits after a policy change. The original policy was to refract all patients that improved with pinhole testing. The policy change was not to refract patients with a visual acuity ≥20/30 unless a specific request was made by the patient. Pre-post analysis of an automated time-motion study was conducted for the data collected 3 months before and 3 months after the policy change occurred on March 30, 2018. Changes to process and wait times were summarized using descriptive statistics and fitted to linear mixed regression models adjusting for appointment type, clinic volume, and daily clinic trends.ResultsOne thousand nine hundred twenty-three visits with 1588 patients were included in the analysis. Mean [SD] age was 65.9 [14.7] years and 892 [56.2%] were women. After the policy change, technician process time decreased by 2.9 min (p < 0.0001) while daily clinical patient volume increased from 51.9 ± 16.8 patients to 58.4 ± 17.4 patients (p < 0.038). No significant difference was found in total wait time (p = 0.18) or total visit time (p = 0.83).ConclusionsReal-time locating systems are effective at capturing clinical flow data and assessing clinical practice change initiatives. The refraction policy change was associated with reduced technician process time and overall the clinic was able to care for 7 more patients per day without significantly increasing patient wait time.

Nursing Workflow Change in a COVID-19 Inpatient Unit Following the Deployment of Inpatient Telehealth: Observational Study Using a Real-Time Locating System.

BackgroundThe COVID-19 pandemic prompted widespread implementation of telehealth, including in the inpatient setting, with the goals to reduce potential pathogen exposure events and personal protective equipment (PPE) utilization. Nursing workflow adaptations in these novel environments are of particular interest given the association between nursing time at the bedside and patient safety. Understanding the frequency and duration of nurse-patient encounters following the introduction of a novel telehealth platform in the context of COVID-19 may therefore provide insight into downstream impacts on patient safety, pathogen exposure, and PPE utilization.ObjectiveThe aim of this study was to evaluate changes in nursing workflow relative to prepandemic levels using a real-time locating system (RTLS) following the deployment of inpatient telehealth on a COVID-19 unit.MethodsIn March 2020, telehealth was installed in patient rooms in a COVID-19 unit and on movable carts in 3 comparison units. The existing RTLS captured nurse movement during 1 pre- and 5 postpandemic stages (January-December 2020). Change in direct nurse-patient encounters, time spent in patient rooms per encounter, and total time spent with patients per shift relative to baseline were calculated. Generalized linear models assessed difference-in-differences in outcomes between COVID-19 and comparison units. Telehealth adoption was captured and reported at the unit level.ResultsChange in frequency of encounters and time spent per encounter from baseline differed between the COVID-19 and comparison units at all stages of the pandemic (all P<.001). Frequency of encounters decreased (difference-in-differences range –6.6 to –14.1 encounters) and duration of encounters increased (difference-in-differences range 1.8 to 6.2 minutes) from baseline to a greater extent in the COVID-19 units relative to the comparison units. At most stages of the pandemic, the change in total time nurses spent in patient rooms per patient per shift from baseline did not differ between the COVID-19 and comparison units (all P>.17). The primary COVID-19 unit quickly adopted telehealth technology during the observation period, initiating 15,088 encounters that averaged 6.6 minutes (SD 13.6) each.ConclusionsRTLS movement data suggest that total nursing time at the bedside remained unchanged following the deployment of inpatient telehealth in a COVID-19 unit. Compared to other units with shared mobile telehealth units, the frequency of nurse-patient in-person encounters decreased and the duration lengthened on a COVID-19 unit with in-room telehealth availability, indicating “batched” redistribution of work to maintain total time at bedside relative to prepandemic periods. The simultaneous adoption of telehealth suggests that virtual care was a complement to, rather than a replacement for, in-person care. However, study limitations preclude our ability to draw a causal link between nursing workflow change and telehealth adoption. Thus, further evaluation is needed to determine potential downstream implications on disease transmission, PPE utilization, and patient safety.

Design and Implementation of Real-Time Localization System (RTLS) Based on UWB and TDoA Algorithm.

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets.

Use of a Real-Time Locating System to Assess Internal Medicine Resident Location and Movement in the Hospital

The patient-physician clinical encounter is the cornerstone of medical training, yet residents spend as little as 12% of their time in direct patient contact. To use a real-time locating system (RTLS) to characterize intern work experiences in the hospital, understand factors associated with time spent at patients' bedsides, and inform future interventions to increase time spent with patients. This cross-sectional study was conducted from July 1, 2018, to June 30, 2019 (ie, the academic year 2018-2019). Internal medicine residents from postgraduate year 1 (interns) at an academic medical center wore an infrared badge that recorded location and duration (eg, patient room, ward hall, physician workroom). Data were analyzed from September 1, 2020, to August 30, 2021. Main outcome was time (in minutes) at the bedside; the unit of analysis was a 24-hour intern day or interval of time within the day (eg, rounding period). Descriptive statistics are reported overall, by intern, and for 5 clinical service categories. Multilevel modeling assessed the association of intern, service, and calendar time with time spent at the bedside. Data from 43 of 52 interns (82.7%) encompassing 95 275 hours of observations were included for analyses. Twenty-six interns (60.5%) were women. Interns were detected for a mean (SD) of 722.8 (194.4) minutes per 24-hour period; 13.4% of this time was spent in patient rooms (mean [SD] time, 96.8 [57.2] minutes) and 33.3% in physician workrooms (mean [SD] time, 240.9 [228.8] minutes). Mean percentage of time at the bedside during a 24-hour period varied among interns from 8.8% to 18.3%. Mean (SD) percentage of time at the bedside varied by service for the 24-hour period from 11.7% (6.6%) for nononcology subspecialties to 15.4% (6.0%) for oncology, and during rounds from 8.0% (12.4%) for nononcology subspecialties to 26.5% (12.1%) for oncology. In multilevel modeling, the individual intern accounted for 8.1% of overall variance in time spent at the bedside during a 24-hour period, and service accounted for 18.0% of variance during rounds. The findings of this cross-sectional study support previous evidence suggesting that interns spend only a small proportion of time with hospitalized patients. The differences in time spent in patients' rooms among interns and during rounds constitute an opportunity to design interventions that bring trainees back to the bedside.

UWB-based Real-Time 3D High Precision Localization System

Ultra-Wideband (UWB) positioning technique featuring high accuracy and low cost has been drawing increasing interest. Angel of arrival (AOA), time difference of arrival (TDOA) and time of arrival (TOA) are common methods for positioning. With DecaWave1000 ranging systems, which applies TOA method, sub-meter accuracy can be achieved easily. However, the traditional DW1000 UWB localization system is unscalable and the ranging stability remains improving. In this paper, a brand new Scalable Multi-Base Multi-Tag (SMBMT) scheme based on traditional DW1000 UWB localization system is proposed to improve the system’s efficiency and scalability. Then an effective data processing algorithm Data Kalman Filter is offered using ranging information only, to diminish the ranging error in the system. By changing the system equation, Data KF is also able to optimize the dynamic trajectory. Finally, the proposed system is tested and the algorithm is analysed. The result shows that not only does applying SMBMT scheme improves the scalability and ranging efficiency of UWB localization system but also increases the accuracy and stability in positioning.

A multi-sensor architecture combining human pose estimation and real-time location systems for workflow monitoring on hybrid operating suites

Despite the advancements to improve patient safety, a significant number of errors still occur in Operating Suites (OS). To improve medical decision-making and the resulting quality of care, it is essential to monitor and understand medical activities’ workflow and interactions. Although some strategies employ different sensor devices, their focus is not on generating complete workflow information. They only combine different data sources to generate their final output, lacking at least one piece of information from a workflow. To tackle this challenge, this paper presents ▪ , a distributed architecture model for sensor data acquisition and processing. ▪ ’s main contribution lies in its multi-sensor data fusion algorithms to extract a computational representation of activities in surgical procedures. In addition, ▪ is flexible to accommodate different deployment configurations,combining depth cameras, Ultra-Wideband positioning systems,and deep learning-based human pose estimation (HPE) mechanisms. The workflow monitoring mechanism was deployed in an actual hybrid OS for an extensive evaluation of the proposal. Our experiments demonstrate that the architecture can capture the information required to monitor the surgical workflow. In particular, the proposed HPE methodology accurately detects the poses of medical staff members, with a maximum error of 5cm.

EdgeLoc: A Robust and Real-Time Localization System Toward Heterogeneous IoT Devices

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments, and localization time restricted by computation resources. This article proposes EdgeLoc, a robust and real-time indoor localization system toward heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in twofold. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this work proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multistep dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33 600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint data set. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

Indoor Positioning and Tracking by Coupling IMU and UWB with the Extended Kalman Filter

The goal of making each related object “location aware,” or “answering the where,” could boost the value of Internet of Things applications (IoT). Ultra-wideband (UWB) is applied in many services. UWB is used as the Real-Time Location System (RTLS), empowering the influence of things to come. Its reception in consumer smartphones and industrial indoor frameworks has made the RF technology watch regardless of the industry. It evident that understanding UWB is indispensable to planning future-evidence target tracking and indoor positioning systems with high accuracy that carries indoor insight into the most confounded assembled conditions. The target’s location is estimated by using the Iterative method, which uses time of arrival (TOA) values. The Mean Square Error is calculated and compared between 2D and 3D environments. This paper also reports the design of an object tracker that uses Inertial Measurement Unit (IMU) and Extended Kalman Filter (EKF). The performance of the EKF is displayed using simulated target tracking in a 3D environment.

Indoor Location Data for Tracking Human Behaviours: A Scoping Review.

Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.

Abstract WP93: An Internet Of Things Based Real Time Location System With Machine Learning For Coordination Of Acute Stroke Endovascular Intervention: A Proof-of-concept Study

Introduction: The delivery of mechanical thrombectomy in acute stroke can be challenging because it requires coordination between patient travel and multiple stroke team members across different locations in the hospital. A Real Time Location System (RTLS) can facilitate a streamlined workflow by determining a patient’s location, but the required infrastructure can be costly to set up. We investigated the feasibility of a machine learning based RTLS using an Internet Of Things (IOT) device prototype to coordinate acute stroke endovascular intervention. Methods: In this proof-of-concept study, study investigators programmed an IOT device to collect WiFi signals from different hospital zones relevant to the mechanical thrombectomy workflow. Different machine learning (ML) algorithms were trained on location labelled training WiFi signals to predict location. The accuracies of the different ML algorithms to predict unlabeled testing WiFi signals collected on a separate day were measured, in percentages. Results: The training and testing datasets contained 1,179 and 293 samples respectively, with 2,516 and 2,618 unique WiFi signals detected in each dataset. The trained neural network, random forest, support vector machine, K-nearest neighbors, decision tree and ensemble classifiers achieved accuracies of 96.6%, 96.2%, 97.3%, 97.3%, 83.6% and 97.3% on the testing dataset respectively. Additionally, the neural network model produced estimates of uncertainty, which were significantly associated with the correctness of predictions based on logistic regression analysis (p &lt; 0.001). Conclusions: An IOT based real time location system with machine learning can accurately predict locations within the hospital, demonstrating immense potential for the future applications of streamlining delivery of acute stroke endovascular intervention.

Real-Time Multi-Car Localization and See-Through System

Real-Time Multi-Car Localization and See-Through System

Functional analysis of an Optical Real Time Locating System in production environments

Driven by the fourth industrial revolution (Industry 4.0), Real Time Location Systems (RTLS) are gaining importance. Holistic information about the position and movement of persons, vehicles and goods is an essential prerequisite for being able to efficiently automate entire process chains in the future with increasing machine autonomy. RTLS technologies enable the spatial and temporal localisation of objects within an environment. The current technological standard in the field of RTLS is based on anchors and transponders (e.g. Ultra Wide Band (UWB) systems), which use radio frequency (RF) signals to determine positions. However, the need for additional technical equipment is also a major weakness of these systems. In this paper, a new technological approach for real-time localisation of objects for industrial applications is presented. The proposed optical RTLS (ORTLS) is based on a decentralised sensor network, which enables the positioning of persons, vehicles and objects in industrial environments by means of artificial intelligence (AI) based object detection. In order to be able to use the system for safety-relevant applications in the future, certification must be obtained with regard to applicable regulations. A prerequisite for this is the validation and monitoring of the system performance with regard to the requirements of functional safety. For the analysis and evaluation of the system performance, this paper presents a methodology for the analysis of the AI-based detection under consideration of the environmental factors.

Potentials and technical implications of tag based and AI enabled optical real-time location systems (RTLS) for manufacturing use cases

Utilizing real-time position information of individual factory objects is a promising field of action in manufacturing systems. For realizing that, different types of real-time locating systems (RTLS) are available which differ according their inherent characteristics, performance and, thus, their feasibility for value adding manufacturing use cases. The paper introduces an innovative RTLS which utilizes optical AI enhanced sensors to detect and track objects. Detailed technical analyses also in comparison with established tag based ultra-wideband (UWB) RTLS underline the general feasibility and good performance. Finally, the capability profiles of those different RTLS technologies were brought together with the requirements of specific manufacturing use cases in order to derive favourable application fields.

Exploring the Added-Value of Integrating Real-Time Location Systems for Tracking Critical Maintenance Tools

Searching and optimally locating maintenance tools is an important undertaking that influences productivity. The efficiency lost can be high for intensive maintenance processes, particularly in instances where technicians share critical (and expensive) tooling. Real-Time Location Systems (RTLS) can serve as a feasible solution for minimizing travel and search times, by tagging tools, thereby dynamically tracking their spatial location real-time. Moreover, RTLS can be deployed alongside Automated Guided Vehicles (AGV’s) thereby supporting smart material handling.In this paper, we propose a model for exploring the added value of implementing RTLS with AGV to better support maintenance processes in high-demand manufacturing and maintenance shop floors. The NetLogo (multi-agent programmable environment) simulation model mimics interactions of maintenance technicians, tasks, and critical tooling within an aircraft maintenance facility. To evaluate the added value of tagging critical tooling to better enhance efficiency locating tools, we define three use-cases. The first case mimics a current scenario in which critical tools are untagged, hence assuming significant search and travel times are incurred by technicians. The second case explores scenarios where tools are tagged, and trackable within the spatial environment of the hangar. The third case integrates both RTLS and AGVs to support both locating tools, and minimising travel times by technicians. Initial simulations results show significant added value for the second and third experiments, verifying suitability of implementing RTLS for complex maintenance and manufacturing processes.

Empirical findings for the usage of 5G as a basis for real time locating systems (RTLS) in the automotive industry

The digitalization of processes and integration of a digital twin are key factors for a successful system design to realize optimization and control in industrial production. The usage of positioning data is essential to achieve these goal - knowing where tools, products, and parts are in real-time. It enables processes to become intelligent, automated, and logged throughout production. Current radio real-time-locating-systems (RTLS) in manufacturing are often using Ultra-Wideband (UWB) solutions requiring individual infrastructure resulting in high costs. 5G technology is turning its focus from mobile-communication to become a globally standardized industry solution - now including high accuracy positioning. Development shows that the infrastructure already used for communication can potentially be utilized for positioning. This contribution aims to determine the industrial maturity of 5G positioning by performing guided empirical expert interviews based on a structured literature review with 28 experts. Topics such as state-of-the-art, comparison to UWB-RTLS and safety are discussed. Results show that industrial interest in 5G positioning is high and centimeter-level accuracies are pursued. However, practical tests show hardware is in early development, and tests are only performed in controlled environments. Concluding, 5G positioning shows great potential - but industrial pilots are required to validate characteristics in complex scenarios.