Exploring a Novel Tool to Measure Wandering Behavior in the Early Childhood Classroom

Wandering, or random movement, affects cognitive and social skills. However, we lack methods to objectively measure wandering behavior. The purpose of this pilot study was to explore the use of the Ubisense real-time location system (RTLS) in an early childhood setting to explore wandering in typically developing (TD) children ( n = 2) and children with or at risk for developmental disabilities (WA-DD; n = 3). We used the Ubisense RTLS, a tool for capturing locations of individuals in indoor environments, and Fractal Dimension (FD) to measure the degree of wandering or the straightness of a path. Results of this descriptive, observational study indicated the Ubisense RTLS collected 46,229 1-s location estimates across the five children, and TD children had lower FD ( M = 1.36) than children WA-DD ( M = 1.42). Children WA-DD have more nonlinear paths than TD children. Implications for measuring wandering are discussed.

Staff and Facility Utilization in Direct Patient Transfer to the Comprehensive Stroke Center: Testing a Real-Time Location System for Automatic Patient Pathway Characterization

Introduction: Starting reperfusion therapies as early as possible in acute ischemic strokes are of utmost importance to improve outcomes. The Comprehensive Stroke Centers (CSCs) can use surveys, shadowing personnel or perform journal analysis to improve logistics, which can be labor intensive, lack accuracy, and disturb the staff by requiring manual intervention. The aim of this study was to measure transport times, facility usage, and patient–staff colocalization with an automated real-time location system (RTLS).Patients and Methods: We tested IR detection of patient wristbands and staff badges in parallel with a period when the triage of stroke patients was changed from admission to the emergency room (ER) to direct admission to neuroradiology.Results: In total, 281 patients were enrolled. In 242/281 (86%) of cases, stroke patient logistics could be detected. Consistent patient–staff colocalizations were detected in 177/281 (63%) of cases. Bypassing the ER led to a significant decrease in median time neurologists spent with patients (from 15 to 9 min), but to an increase of the time nurses spent with patients (from 13 to 22 min; p = 0.036). Ischemic stroke patients used the most staff time (median 25 min) compared to hemorrhagic stroke patients (median 13 min) and stroke mimics (median 15 min).Discussion: Time spent with patients increased for nurses, but decreased for neurologists after direct triage to the CSC. While lower in-hospital transport times were detected, time spent in neuroradiology (CT room and waiting) remained unchanged.Conclusion: The RTLS could be used to measure the timestamps in stroke pathways and assist in staff allocation.

Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people’s location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one—where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.

An Augmented Reality Symbiosis Software Tool for Sustainable Logistics Activities

Augmented Reality (AR) is an emerging technology in the Industry 4.0 and Logistics 4.0 contexts with an important role in man–machine symbiosis scenarios. Practitioners, although already acquainted with AR technology, are reluctant to adopt AR applications in industrial operations. This stems from the fact that a direct connection that is important for the management of sustainability goals is missing. Moreover, such a connection with economic, social, and environmental sustainability parameters sparsely appears in the AR literature. The proposed research, on one hand, presents an innovative architecture for a stable and scalable AR application that extents state-of-the-art solutions and, on the other hand, attempts to study AR technology within the framework of a sustainable business strategy. The developed system utilizes the Robot Operating System (ROS) alongside an AR mobile application to present an employee navigation scenario in warehouses and production lines. ROS is responsible for mapping the industrial facility, while the AR mobile application identifies the surrounding environment, along with a Real-Time Location System localizes employees in the facility. Finally, ROS identifies the shortest path between the employee and the destination point, while the AR mobile application presents the virtual path for reaching the destination.

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

BackgroundDelivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention.MethodsWe conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction.ResultsML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested.ConclusionsML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

APPLICATION POSSIBILITIES OF RTLS SYSTEM IN PRODUCTION LOGISTICS

Determining the current position of objects in logistics processes (products, unit loads, forklifts, etc.) is the basis for new process development and optimization opportunities (e.g., development of storage strategy, forklift route planning, etc.) that significantly affect the competitiveness of companies. Background information systems that provide real-time data using localization, - or called RTLS (Real Time Location System),- are becoming more widespread. In the dissertation I summarize the operation, physical realization, conditions of use and advantages of a real-time technology that can be applied to a warehouse serving its production, as well as the steps of a possible implementation.