Real-time Location System Research Articles

Monitoring Movements of Ataxia Patient by Using UWB Technology.

Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.

Performance comparison of UCA and UCCA based real-time sound source localization systems using circular harmonics SRP method

Many sound source localization (SSL) algorithms based on circular microphone array (CMA), including uniform circular array (UCA) and uniform concentric circular array (UCCA), have been well developed and verified via computer simulations and offline processing. On the other hand, beamforming in the harmonic domain has been shown to be a very efficient tool for broadband sound source localization due to its frequency-invariant properties. In this paper, design, implementation, and performance of a real-time sound source localization system are discussed. Specifically, we analyze the effect of parameter settings and compare the performance between UCA and UCCA through real-time experiments in a real room. The proposed method shows significant improvement by using UCCA instead of UCA.

High precision real-time location estimates in a real-life barn environment using a commercial ultra wideband chip

Structural changes lead to an increase in the number of dairy cows and dry sows kept per group. This has consequences in how easily a farmer can supervise his herd and may be detrimental to animal welfare, specifically regarding social relations, time budget and area of residence. An automated tracking system can support the farmer in his management activities and can provide the foundation for a scientific assessment of the welfare consequences of large groups. In this study, a relatively simple and inexpensive real time location system (RTLS) was developed with the aim of achieving precise localization of several tags (animals) in real time and in a real barn environment. The RTLS was based on the ultra-wideband (UWB) technology provided by DecaWave and was adapted for a time difference of arrival (TDoA) procedure to estimate the tags’ positions. The RTLS can handle up to a hundred tags simultaneously using a Pure ALOHA random access method at 1-second intervals. The localization of the tags was estimated in 2D on a given fixed height using a constrained Gauss-Newton algorithm to increase accuracy and stability. The performance of the overall system was evaluated in two different dairy barns. To determine the precision of the system, static and dynamic positions measured at withers height of a cow (1.5 m) and closer to the ground mimicking a lying cow were compared with a reference system (theodolite). The 2D deviations between the systems were used as a measure of precision. In addition, the scalability in respect to the number of tags and the size of the observed area was examined in situations with ten tags and the situation with 100 tags was simulated with a ten-fold increase in sampling rate.According to the field test, the system as developed can be used for the individual localization of animals. At withers height, most of the measured locations deviated less than 0.5 m from the localizations as measured by the theodolite. At lower heights, and closer to the corners of the observed area, some localization estimates were somewhat larger. This was also the case close to large metal barn infrastructure. The measured collision rate of 11% for 100 tags was low. In spite of its low price, the system as a whole is therefore promising and ready for a next step, which should include the observation of large groups of real animals on working farms.

Human Real Time Localization System in Underground Mines using UWB

This work proposes an improved Real Time Localization System using UWB technology, for people and asset tracking in a closed environment, particularly in underground mines. The UWB technology has demonstrated a very good performance with a localization error margin below 41 centimeters. The main objective of this work was to develop an affordable, easy to use, and scalable localization system to detect people in underground mines that allows fast location and rescue in case of disaster. It can also improve the mine productivity under normal conditions. Different tests were carried out in mines and urban buildings to validate the design and evaluate the error margin. The test in an underground mine was done at “Casposo Mining”, which is located in the town of Calingasta, San Juan province, Argentina. Finally, an autonomy of 25 hours of continuous used was achieved using a 2000mAh battery.

High Accuracy Ultra Wideband Real Time Location System for Drug Rehabilitation Center

High Accuracy Ultra Wideband Real Time Location System for Drug Rehabilitation Center

Attitudes and Behavior of Health Care Workers Before, During, and After Implementation of Real-Time Location System Technology.

ObjectiveTo assess how staff attitudes before, during, and after implementation of a real-time location system (RTLS) that uses radio-frequency identification tags on staff and patient identification badges and on equipment affected staff’s intention to use and actual use of an RTLS.Participants and MethodsA series of 3 online surveys were sent to staff at an emergency department with plans to implement an RTLS between June 1, 2015, and November 29, 2016. Each survey corresponded with a different phase of implementation: preimplementation, midimplementation, and postimplementation. Multiple logistic regression with backward elimination was used to assess the relationship between demographic variables, attitudes about RTLSs, and intention to use or actual use of an RTLS.ResultsDemographic variables were not associated with intention to use or actual use of the RTLS. Before implementation, poor perceptions about the technology’s usefulness and lack of trust in how employers would use tracking data were associated with weaker intentions to use the RTLS. During and after implementation, attitudes about the technology’s use, not issues related to autonomy and privacy, were associated with less use of the technology.ConclusionReal-time location systems have the potential to assess patterns of health care delivery that could be modified to reduce costs and improve the quality of care. Successful implementation, however, may hinge on how staff weighs attitudes and concerns about their autonomy and personal privacy with organizational goals. With the large investments required for new technology, serious consideration should be given to address staff attitudes about privacy and technology in order to assure successful implementation.

An Accuracy and Real-Time Commercial Localization System in LTE Networks

In this paper, an ARTLoc (accuracy and real-time commercial localization) system is proposed, which is applicable for positioning the Minimization of Drive-Tests(MDT) data in the long-term-evolution(LTE) cellular communication network system. This system utilizes a collaborative filtering algorithm to ensure the robustness and accuracy of the fingerprint database when both base station position and GPS MDT data are abnormal in commercial communication service provider(CSP) MDT localization scene. Then, a fast matching KNN algorithm including coarse matching and fine matching is proposed to improve the location efficiency and reduce the location cost under the premise of ensuring high location accuracy. The results of experiments conducted in an in-service LTE network using more than 1000 LTE base station demonstrate that the proposed technique yields a location accuracy of 65.7 meters(@67%) with DT data and 62.1 meters(@67%) with massive GPS MDT data(over 10 million), which provides at least 184.2% and 145.2% enhancement in accuracy respectively compared to the traditional technology. In addition, our system provides a more than 7 times improvement in location efficiency compared to traditional technology. This proposed localization technique is applicable in network optimization and Operation and Maintenance(O&M) to assist CSP to reduce their operating expense(OPEX) by positioning those MDT data without GPS location.

Leveraging technology to measure, evaluate, and adjust nursing interventions.

One acute care hospital used a real-time location system to determine if a relationship exists between the amount of time nursing staff members spend at the bedside and patient satisfaction. What did they find out?

RSSI Fingerprinting Based iPhone Indoor Localization System Without Apple API

The flourish of mobile communications is driven by the increasing number of subscribers and rapid advance of electronic devices. This brings up many multimedia context-aware services, among which real time locating system (RTLS) has become necessary in many applications. To locate a mobile station (MS), RTLS could apply conventional fingerprinting algorithm using received signal strength indicator (RSSI), which allows a MS to collect RSSI data from beacons sent by access points (APs). However, this method does not work for iPhones because of the lack of open access to Apple application programming interface (API) to obtain RSSI values. This paper proposes an alternative approach, under which APs in the monitoring mode are used to collect RSSI values for semi-beacon packets sent from a MS. We implement the packet capture library, which enables us to obtain RSSI values of semi-beacon packets sent by an iPhone. With a region-based k-nearest neighbor (kNN) localization algorithm, we successfully locate an iPhone user in indoor environment. In addition, experimental results show that the proposed approach outperforms the conventional RSSI fingerprinting approach.

Visible Light Localization Using Conventional Light Fixtures and Smartphones

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near-term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification, and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high-frequency (greater than 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-story office building, parking lot, and grocery store further validates LiTell as an accurate, robust, and ready-to-use indoor localization system.

Taking Advantage of Collective Intelligence and BIM-Based Virtual Reality in Fire Safety Inspection for Commercial and Public Buildings

Commercial and public buildings are more vulnerable to fires because of their complex use functions, large number of centralized occupants, and the dynamic nature of the use of space. Due to the large number of these types of buildings and the limited availability of manpower, annual fire inspections cannot ensure the continuous compliance of fire codes. A crowdsourcing application, iInspect, is proposed in this paper to harvest collective intelligence in order to conduct mass inspection tasks. This approach is supported by building information modeling (BIM) based virtual reality (VR) and an indoor real-time localization system. Based on the International Fire Code and 27 fire inspection checklists compiled by various local authorities, a generic list of inspection items suitable for iInspect is proposed, along with a reputation-based monetary incentive model. A prototype of iInspect was created for Android mobile phones, and a case study was performed in an office building in Tianjin, China, for verification of this crowdsourcing inspection approach.

A Robust RGB-D SLAM System With Points and Lines for Low Texture Indoor Environments

High-performance 6D pose estimation and dense 3D mapping with an RGB-D camera has recently attracted substantial research attention since this type of camera can simultaneously capture RGB and depth information. However, there is an unresolved problem in estimating pose and generating highly accurate 3D maps from challenging indoor scenes. This paper presents a real-time simultaneous localization and mapping (SLAM) system based on the RGB-D camera for indoor mobile robots. Our contributions are fourfold. First, we propose a complete high-accuracy SLAM system based on a combination of information from points and lines, which differs from most solutions that rely on only point features. Second, we propose a novel pose solver to handle point and line correspondences, in which a line-based inliers refinement (LBIR) algorithm is proposed to remove outliers. Third, we construct a unified optimization model to concurrently minimize point and line reprojection errors, and extend it to the bundle adjustment (BA) method. Fourth, extensive experiments demonstrate the robustness, accuracy, and real-time performance of the proposed system on public TUM datasets and real world scenes. The empirical results show that the proposed system achieves a comparable or better performance than state-of-the-art methods. Notably, our system can operate in nearly texture-less scenes, while other methods are prone to failure.

Introduction of a real time location system to enhance the warehouse safety and operational efficiency

As a real time location system (RTLS) can provide positioning and tracking of forklifts and other mobile entities in a warehouse, the integration of RTLS into a data system has a significant potential to enhance safety and improve efficiency. The objective of this research is to demonstrate how RTLS technology can be leveraged to enhance the warehouse safety and operational efficiency via a real warehouse case study. The research is implemented using a novel three-phase framework to introduce the RTLS technology in the warehouse. The first phase evaluates available RTLS technologies. Market research has been conducted to compare different RTLS technologies, and the outcome of this phase suggests that the ultra-wide band (UWB) is the best technology for indoor positioning and tracking in the warehouse with respect to several metrics. The second phase is the technology implementation, which aims to integrate RTLS with other existing warehouse operation systems, i.e., the warehouse management system (WMS) and the forklift fleet management system (FFMS). In this phase, light is shed on current challenges and proposed solutions of RTLS data accuracy and synchronization. The final phase is post-implementation, in which several methods and data visualization tools are proposed to tackle safety and operational issues. A real warehouse in the US has been used as a case study to evaluate the proposed framework. In particular, the following analyses have been conducted using the actual RTLS data: (1) harshness in braking, (2) compliance to routing policies, (3) driver patterns in intersections, (4) congestion identification and prevention, (5) speed per zone, (6) impact analysis, and (7) faults analysis. The results suggest that the proposed framework has a great potential to advance the current warehouse solutions one step forward to realize smart warehouse operations.

Obstructive Sleep Apnea and Ambulatory Surgery: Who Is Truly at Risk?

Obstructive Sleep Apnea and Ambulatory Surgery: Who Is Truly at Risk?

A Framework for an Intelligent and Personalized Fire Evacuation Management System.

Many research studies have focused on fire evacuation planning. However, because of the uncertainties in fire development, there is no perfect solution. This research proposes a fire evacuation management framework which takes advantage of an information-rich building information modeling (BIM) model and a Bluetooth low energy (BLE)-based indoor real-time location system (RTLS) to dynamically push personalized evacuation route recommendations and turn-by-turn guidance to the smartphone of a building occupant. The risk score (RS) for each possible route is evaluated as a weighted summation of risk level index values of all risk factors for all segments along the route, and the route with the lowest RS is recommended to the evacuee. The system will automatically re-evaluate all routes every 2 s based on the most updated information, and the evacuee will be notified if a new and safer route becomes available. A case study with two testing scenarios was conducted for a commercial office building in Tianjin, China, in order to verify this framework.

Intelligent detection technology for leakage bag of baghouse based on distributed optical fiber sensor

In order to overcome the deficiency of traditional methods for damaged bag identification and location of baghouse in environmental protection industry, a real-time online identification and location system for leak bag of bag filter based on distributed optical fiber sensing technology of phase-sensitive optical time-domain reflectometer (Φ-OTDR) is proposed. Through the design of laying mode of optical fiber in filter bag of baghouse, the program flow of identifying and locating damaged filter bag is designed, and the real-time monitoring and positioning of filter bags is realized by distributed optical fiber sensing system. Wavelet packet decomposition (WPD) method is used to obtain the energy spectrum and energy entropy. Taking the energy ratio of the third sub-band frequency signal and the energy entropy as eigenvectors, the characteristic differences of optical fiber vibration signals between different types of good bags and different types of damaged bags with different leakage hole size and position are analyzed. The results show that the eigenvectors extracted by the method used in this paper can effectively describe the characteristics of the airflow vibration signals in the filter bags. Back propagation (BP) neural network algorithm is used to recognize the optical fiber vibration signals in damaged bags with different leakage hole sizes and positions. In addition, quantitative analysis method is used to identify the sample signals in different types of damaged bags. First, the airflow vibration signals in damaged bags with different leakage hole positions are identified while the leakage hole size remains the same. Second, the airflow vibration signals in leakage bags with different leakage hole sizes are identified while the leakage hole position remains the same. Finally, the BP neural network classifier is trained by the characteristics of the vibration signals in a filter bag, and it is used to identify the remaining field testing signals. Each type of vibration event is identified 10 times and the average recognition rate is calculated. The results show that the proposed classifier maintains high recognition stability and a high recognition rate for different types of damaged bags is obtained for the proposed method, which can reach higher than 90%.

Patient-Care Team Contact Patterns Impact Treatment Length of Stay in the Emergency Department.

Real-time location systems (RTLS) has found extensive application in the healthcare setting, that is shown to improve safety, save cost, and increase patient satisfaction. More specifically, some studies have shown the efficacy of RTLS leading to an improved workflow in the emergency department. However, due to substantial implementation costs of such technologies, hospital administrators show reluctance in RTLS adoption. Our previous preliminary studies with RFID data in the emergency department (ED) demonstrated for the first time the quantification of `patient alone time' and its relationship to outcomes such as 30-day hospitalization. In this study, we use ED RTLS data to analyze patient-care team contact time (PCTCT) and its relationship to the total treatment length of stay (LOS) in ED. An observational cohort study was performed in the ED using RTLS data from Jan 17 - Sep 17, 2017, which included a total of 51,697 patients. PCTCT within the first hour of a patient's placement in a treatment bed was calculated and its relationship to treatment LOS was analyzed while controlling for confounding factors affecting treatment LOS. Results show that treatment LOS is highly correlated with the ED crowding captured by the patient-perprovider ratio, negatively correlated to the physician and resident visit frequency, and positively correlated to nurse visit frequency. The results can inform designing new guidelines for ideal patient-care team interactions and be used to determine optimal ED staffing levels and care team composition for effective care delivery.

Improvement of RTLS Performance Using Recursive RF Active Echo Algorithm

When it comes to indoor Real-Time Location Systems (RTLS) for an Automatic Guided Vehicle (AGV), the most important factor is to minimize the location estimation error. In industrial sites, centimetre-level accuracy is required for the AGV. Since the duration for measuring the position of an AGV is slower than the speed of arriving RF signals, the margin of error inevitably becomes larger than in systems that use low-speed media such as ultrasonic waves. To minimize such errors, a short-range location estimation Recursive RF Active Echo (RRFAE) algorithm is presented. Increasing the time ratio by repeatedly and interactively reflecting the signal can reduce measurement error. That is, there is an effect of expanding the measurement distance by the number of iterations. We propose and implement a precise positioning algorithm in an indoor environment where many reflections exist. The error analysis of the RRFAE algorithm after hardware correction shows that the level of the RRFAE's errors is only one tenth that of an RTLS system that uses other RF signals.

Learning to Recognize Unmodified Lights with Invisible Features

To enable accurate indoor localization at low cost, recent research in visible light positioning (VLP) proposed to employ existing ceiling lights as location landmarks, and use smartphone cameras or light sensors to identify the different lights using statistical visual/optical features. Despite the potential, we find such solutions are unreliable: the features are easily corrupted with a slight rotation of the smartphone, and are not discriminative enough for many practical light models with different size/shape/intensity. In this work, we propose Auto-Litell to resolve these critical challenges and make VLP truly robust. Auto-Litell builds a customized deep-learning neural network model to automatically distill the "invisible" visual features from the lights, which are resilient to smartphone orientation and light models. Moreover, Auto-Litell introduces a Light-CycleGAN to generate "fake" light images to augment the training data, so as to relieve human labors in data collection and labeling. We have implemented Auto-Litell as a real-time localization and navigation system on Android. Our experiments demonstrate Auto-Litell's high accuracy in discriminating the lights in the same building, and high reliability across a variety of practical usage scenarios.

Real-time Location System Feasibility Study

Real-time Location System Feasibility Study