Automatic Identification and Data Capture

The increasing availability and capabilities of mobile phones make them a feasible means of data collection. Electronic Data Capture (EDC) systems have been used widely for public health monitoring and surveillance activities, but documentation of their use in complicated research studies requiring multiple systems is limited. This paper shares our experiences of designing and implementing a complex multi-component EDC system for a community-based four-armed cluster-Randomised Controlled Trial in the rural plains of Nepal, to help other researchers planning to use EDC for complex studies in low-income settings. We designed and implemented three interrelated mobile phone data collection systems to enrol and follow-up pregnant women (trial participants), and to support the implementation of trial interventions (women's groups, food and cash transfers). 720 field staff used basic phones to send simple coded text messages, 539 women's group facilitators used Android smartphones ...

Digitization of complex Cultural Heritage sites requires the integration of several strategies to achieve a comprehensive description of the site. This is the case of the digitization project of the Appian Way in Rome involving a section of 12 km from Capo di Bove to Frattocchie under the superintendence of the Parco Archeologico dell'Appia Antica. This paper presents some first results of the project with a specific focus on the acquisition of the primary data needed for the further steps of the project. In particular, the paper discusses the integration of different sensors such as lidar-based Mobile Mapping Systems, spheric (360°) cameras and digital (frame) calibrated cameras, for the reconstruction of point clouds to be used as input data for the digitization process. A specific attention is paid both on acquisition speed and to accuracy of the obtained results. The paper presents also some results showing the transformation of the acquired data into a GIS system to be used as a support tool for decision making of the public administration.

Since the 1970s, automatic identification (auto-ID) technologies have been evolving to revolutionise the way people live and work. Previous research has not addressed auto-ID technological innovation as a field of study, despite its growing importance on consumer, business and government electronic commerce (EC) applications. This paper is specifically concerned with five auto-ID technologies, bar codes, magnetic-stripe card, smart card, biometrics and radiofrequency identification (RF/ID) tags and transponders. Using multiple embedded case studies and applying the fundamental concepts of the systems of innovation (SI) approach, the overall aim is to understand the selection environment of the auto-ID industry. The results show that industry competition encourages the coexistence of numerous auto-ID technologies, dispelling the idea that one superior technology will make all others obsolete. This can be seen by the trends of migration, integration and convergence that are occurring in auto-ID innovation today. The significance of the study rests in its practical contributions. Stakeholders will benefit by becoming more aware of the opportunities in the market for the recombination of one or more auto-ID techniques. Additionally, suppliers of auto-ID system components will understand the new paradigm where various auto-ID firms can share in the same trajectory successfully.

Mobile Mapping Systems (MMSs) stand out as the preferred solution for achieving highly precise 3D environmental models, particularly in urban planning, highway mapping, asset inventory, corridor mapping, traffic safety evaluation, autonomous vehicle, digital twin, and emergency response mapping where traditional aerial or satellite surveys often fall short in providing precise data. Understanding the intricate factors that impact the accuracy of mobile mapping is pivotal to harnessing the full potential of this advanced measurement technique. This study analyzes the spatial accuracy of geographical data produced by the mobile mapping method, considering factors such as the speed of the mobile mapping tool, measurement time difference, camera shooting distance of the produced data, and differences in picture shooting distances. The acquired results were examined for their applicability in the production of inventory along the highway route, revealing their practical usability through analysis and findings. This investigation delves into the proficiency and precision benchmarks of mobile mapping systems, specifically in the context of creating road inventory and supporting decision-making for road systems. The study discusses the usability and accuracy criteria of mobile mapping systems for creating transportation inventory and decision support systems.

We have developed a vendor agnostic, full disclosure system for the capture, display, and storage of operative systems data. This system allows door to door capture of data from multiple sources including monitors from competing vendors, integration under a single platform, and storage for future use. Full disclosure functionality includes the ability to retrieve and display archived data including full waveform and trend physiologic data, synchronized to the surgical video and other OR devices and data sources.

Background Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics. Methodology A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motio...

Background Governments, universities and pan-African research networks are building durable infrastructure and capabilities for biomedical research in Africa. This offers the opportunity to adopt from the outset innovative approaches and technologies that would be challenging to retrofit into fully established research infrastructures such as those regularly found in high-income countries. In this context we piloted the use of a novel mobile digital health platform, designed specifically for low-resource environments, to support high-quality data collection in a clinical research study. Objective Our primary aim was to assess the feasibility of a using a mobile digital platform for clinical trial data collection in a low-resource setting. Secondarily, we sought to explore the potential benefits of such an approach. Methods The investigative site was a research institute in Nairobi, Kenya. We integrated an open-source platform for mobile data collection commonly used in the developing world with an open-source, standard platform for electronic data capture in clinical trials. The integration was developed using common data standards (Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model), maximising the potential to extend the approach to other platforms. The system was deployed in a pharmacokinetic study involving healthy human volunteers. Results The electronic data collection platform successfully supported conduct of the study. Multidisciplinary users reported high levels of satisfaction with the mobile application and highlighted substantial advantages when compared with traditional paper record systems. The new system also demonstrated a potential for expediting data quality review. Discussion and Conclusions This pilot study demonstrated the feasibility of using a mobile digital platform for clinical research data collection in low-resource settings. Sustainable scientific capabilities and infrastructure are essential to attract and support clinical research studies. Since many research structures in Africa are being developed anew, stakeholders should consider implementing innovative technologies and approaches.

Background Governments, universities and pan-African research networks are building durable infrastructure and capabilities for biomedical research in Africa. This offers the opportunity to adopt from the outset innovative approaches and technologies that would be challenging to retrofit into fully established research infrastructures such as those regularly found in high-income countries. In this context we piloted the use of a novel mobile digital health platform, designed specifically for low-resource environments, to support high-quality data collection in a clinical research study. Objective Our primary aim was to assess the feasibility of a using a mobile digital platform for clinical trial data collection in a low-resource setting. Secondarily, we sought to explore the potential benefits of such an approach. Methods The investigative site was a research institute in Nairobi, Kenya. We integrated an open-source platform for mobile data collection commonly used in the developing world with an open-source, standard platform for electronic data capture in clinical trials. The integration was developed using common data standards (Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model), maximising the potential to extend the approach to other platforms. The system was deployed in a pharmacokinetic study involving healthy human volunteers. Results The electronic data collection platform successfully supported conduct of the study. Multidisciplinary users reported high levels of satisfaction with the mobile application and highlighted substantial advantages when compared with traditional paper record systems. The new system also demonstrated a potential for expediting data quality review. Discussion and Conclusions This pilot study demonstrated the feasibility of using a mobile digital platform for clinical research data collection in low-resource settings. Sustainable scientific capabilities and infrastructure are essential to attract and support clinical research studies. Since many research structures in Africa are being developed anew, stakeholders should consider implementing innovative technologies and approaches.

Electronic Data Capture (EDC) has become a common a proven tool for data collection and management in clinical trials. Thus, understanding the principles and methods for EDC use has become a major component of clinical data management (CDM) professional practice. This chapter focuses on using the EDC system and accruing data to support study conduct, maintaining an EDC system during a study, and concluding active data collection through database lock. The regulatory basis for minimum standard and recommended best practices are discussed.

Assessing the quality of fit of a statistical model to data is a necessary step for conducting safe inference. 2. We introduce R2ucare, an R package to perform goodness-of-fit tests for open single-and multi-state capture-recapture models. R2ucare also has various functions to manipulate capture-recapture data. 3. We remind the basics and provide guidelines to navigate towards testing the fit of capturerecapture models. We demonstrate the functionality of R2ucare through its application to real data.

The increasing availability and capabilities of mobile phones make them a feasible means of data collection. Electronic Data Capture (EDC) systems have been used widely for public health monitoring and surveillance activities, but documentation of their use in complicated research studies requiring multiple systems is limited. This paper shares our experiences of designing and implementing a complex multi-component EDC system for a community-based four-armed cluster-Randomised Controlled Trial in the rural plains of Nepal, to help other researchers planning to use EDC for complex studies in low-income settings. We designed and implemented three interrelated mobile phone data collection systems to enrol and follow-up pregnant women (trial participants), and to support the implementation of trial interventions (women's groups, food and cash transfers). 720 field staff used basic phones to send simple coded text messages, 539 women's group facilitators used Android smartphones ...

Monitoring of clinical trials includes several disciplines, stakeholders, and skill sets. The aim of the present study was to identify database changes and data entry errors to an electronic data capture (EDC) clinical trial database, and to access the impact of the changes. To accomblish the aim, Target e*CRF was used as the EDC tool for a multinational, dose-finding, multicenter, double-blind, randomized, parallel, placebo-controlled trial to investigate efficacy and safety of a new treatment in men with lower urinary tract symptoms associated with benign prostatic hyperplasia. The main errors observed were simple transcription errors from the paper source documents to the EDC database. This observation was to be expected, since every transaction has an inherant error rate. What and how to monitor must be assessed within the risk-based monitoring section of the comprehensive data monitoring plan. With the advent of direct data entry, and the elimination of the requirement to transcribe from a paper source record to an EDC system, error rates should go down dramatically. In addition, protocol violations and data outside the normal range can be identified at the time of data entry and not days, weeks, and months after the fact.

We analyzed AVL stop level data from a rural transit system to identify data completeness and systematic data capture failures. Systematic data loss could compromise the validity of further analyses of the data, such as schedule adherence or run time performance. We audited the data to identify missing values and possible data recording errors. The frequency of missing values was analyzed as a function of trip start time, stop number, day of the week, and last reported seconds late. We also perform an outlier and extreme value analysis as a function of missing records per trip. We conclude that there are systematic data capture errors in the system that needs to be addressed before further studies, such as run time analysis can be performed. Given the widespread adoption of an AVL system by rural transit system, it is recommended that detail data completeness analysis becomes routine before using the data generated to perform other studies. Contribution/ Originality This is, to our knowledge, the first paper to review data from a rural transit system and offer relatively easy and straight forward tools for data completeness analysis. The methodology could be replicated by other transit systems with limited data analysis resources. This research would be of interest to Transit systems that uses AVL technology, particularly rural transit systems as well as AVL hardware and software providers.

Background Governments, universities and pan-African research networks are building durable infrastructure and capabilities for biomedical research in Africa. This offers the opportunity to adopt from the outset innovative approaches and technologies that would be challenging to retrofit into fully established research infrastructures such as those regularly found in high-income countries. In this context we piloted the use of a novel mobile digital health platform, designed specifically for low-resource environments, to support high-quality data collection in a clinical research study. Objective Our primary aim was to assess the feasibility of a using a mobile digital platform for clinical trial data collection in a low-resource setting. Secondarily, we sought to explore the potential benefits of such an approach. Methods The investigative site was a research institute in Nairobi, Kenya. We integrated an open-source platform for mobile data collection commonly used in the developing world with an open-source, standard platform for electronic data capture in clinical trials. The integration was developed using common data standards (Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model), maximising the potential to extend the approach to other platforms. The system was deployed in a pharmacokinetic study involving healthy human volunteers. Results The electronic data collection platform successfully supported conduct of the study. Multidisciplinary users reported high levels of satisfaction with the mobile application and highlighted substantial advantages when compared with traditional paper record systems. The new system also demonstrated a potential for expediting data quality review. Discussion and Conclusions This pilot study demonstrated the feasibility of using a mobile digital platform for clinical research data collection in low-resource settings. Sustainable scientific capabilities and infrastructure are essential to attract and support clinical research studies. Since many research structures in Africa are being developed anew, stakeholders should consider implementing innovative technologies and approaches.

In recent years, a new generation of instruments has appeared that are motion-based capture. These systems are based on a combination of techniques, among which LIDAR stands out. In this article we present a new proposal for a 3D model generation instrument based on videogrammetry. The prototype designed consists of two cameras connected to a computer system. One of the cameras is in charge of running VisualSLAM and guiding the user in real time at the moment of data acquisition; the other camera, with a higher resolution, saves the images and, thanks to a refined 3D-Based frame selection algorithm, processes them using automatic photogrammetric procedures, generating one or more point-clouds that are integrated to give way to a high-density and high-precision 3D colour point-cloud. The paper evaluates the proposal with four case studies: two of an urban nature and two related to historical heritage. The resulting models are confronted with the Faro Focus3D X330 laser scanner, class...

In recent years, a new generation of instruments has appeared that are motion-based capture. These systems are based on a combination of techniques, among which LIDAR stands out. In this article we present a new proposal for a 3D model generation instrument based on videogrammetry. The prototype designed consists of two cameras connected to a computer system. One of the cameras is in charge of running VisualSLAM and guiding the user in real time at the moment of data acquisition; the other camera, with a higher resolution, saves the images and, thanks to a refined 3D-Based frame selection algorithm, processes them using automatic photogrammetric procedures, generating one or more point-clouds that are integrated to give way to a high-density and high-precision 3D colour point-cloud. The paper evaluates the proposal with four case studies: two of an urban nature and two related to historical heritage. The resulting models are confronted with the Faro Focus3D X330 laser scanner, class...

Purpose This article examines the potential use of personal digital assistant (PDA) data capture systems for real-time linear monitoring of health-related quality of life (HRQOL) in prostate cancer research and clinical care. Methods We discuss the benefits and potential issues of using PDA data capture in the clinical health care setting. In addition, we describe the development and potential use of a PDA data capture system specific to managing HRQOL in prostate cancer treatment. Conclusion Follow-up health care clinics require a practical and systematic process of HRQOL data capture and analysis. Traditional paper questionnaire data capture is problematic. Data manipulation required for clinical decision-making is impractical for patient feedback on same-day clinic visits. Furthermore, the process of transforming paper questionnaire data to analysis-quality data can compromise data integrity. In contrast, research findings confirm the acceptability, ease of use, and reliability o...

Multi-view data capture permits free-viewpoint video (FVV) content creation. To this end, several users must capture video streams, calibrated in both time and pose, framing the same object/scene, from different viewpoints. New-generation network architectures (e.g. 5G) promise lower latency and larger bandwidth connections supported by powerful edge computing, properties that seem ideal for reliable FVV capture. We have explored this possibility, aiming to remove the need for bespoke synchronisation hardware when capturing a scene from multiple viewpoints, making it possible through off-the-shelf mobiles. We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures. We have designed an edge computing unit that supervises the relaying of timing triggers to and from multiple mobiles, in addition to synchronising frame harvesting. We empirically show the benefits of our edge computing unit by analysing latencies and show the quality of 3D reconstruction outputs against an alternative and popular centralised solution based on Unity3D.

Three Dimensional (3D) models are widely used in clinical applications, geosciences, cultural heritage preservation, and engineering; this, together with new emerging needs such as building information modeling (BIM) develop new data capture techniques and devices with a low cost and reduced learning curve that allow for non-specialized users to employ it. This paper presents a simple, self-assembly device for 3D point clouds data capture with an estimated base price under €2500; furthermore, a workflow for the calculations is described that includes a Visual SLAM-photogrammetric threaded algorithm that has been implemented in C++. Another purpose of this work is to validate the proposed system in BIM working environments. To achieve it, in outdoor tests, several 3D point clouds were obtained and the coordinates of 40 points were obtained by means of this device, with data capture distances ranging between 5 to 20 m. Subsequently, those were compared to the coordinates of the same t...

This paper was written to create awareness among the library
professionals about AIDC (Automatic Identification and Data Capture)
technologies and their potential application in libraries/information
centres. Data entry is an inseparable part of the library operations. This
is an repetitive job and slows down the speed of the system as a whole.
So, AIDC technologies have been described in this paper as an
probable solution. Each technology has been introduced individually
and its working has been described. The technologies which have been
included are, bar code, RFID, biometrics, OCR, MICR, magnetic strips,
smart card and voice recognition. After that the applicability of each
individual technology in libraries has been described briefly. Towards
the conclusion of the paper the future of AIDCs have been discussed in
view of the changing scenario with the introduction of born digital
documents and internet.