scholarly journals Usage of Augmented Reality for Improved Human-Machine Interaction and Real-Time Error Correction of Laboratory Units

2022 ◽  
pp. 263-274
Author(s):  
Vadym Bilous ◽  
J. Philipp Städter ◽  
Marc Gebauer ◽  
Ulrich Berger
Keyword(s):  
Augmented Reality ◽  
Error Correction ◽  
Real Time ◽  
Technical Assistance ◽  
Data Transfer ◽  
Graphical Form ◽  
Human Machine Interaction ◽  
Laboratory Unit ◽  
Industrial Systems ◽  
Machine Interaction

AbstractFor future innovations, complex Industry 4.0-technologies need to improve the interaction of humans and technology. Augmented Reality (AR) has a significant potential for this task by introducing more interactivity into modern technical assistance systems. However, AR systems are usually very expensive and thus unsuitable for small and medium-sized enterprises (SMEs). Furthermore, the machine's reliable data transfer to the AR applications and the user activity indication appear to be problematic. This work proposes a solution to these problems. A simple and scalable data transfer from industrial systems to Android applications has been developed.The suggested prototype demonstrates an AR application for troubleshooting and error correction in real-time, even on mobile or wearable devices, while working in a laboratory unit to simulate and solve various errors. The unit components (small garage doors) are equipped with sensors. The information about the state of the system is available in real-time at any given moment and is transmitted to a mobile or wearable device (tablet or smart glass) equipped with AR application. The operator is enabled to preview the required information in a graphical form (marks and cursors). Potential errors are shown and solved with an interactive manual. The system can be used for training purposes to achieve more efficient error correction and faster repairing.

scholarly journals A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3582
Author(s):  
Sierra N. Young
Keyword(s):  
High Throughput ◽  
Data Transfer ◽  
Meta Analysis ◽  
Human Machine Interaction ◽  
System Complexity ◽  
Potential Barriers ◽  
Processing Pipeline ◽  
Future Improvement ◽  
High Throughput Phenotyping ◽  
Machine Interaction

This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.

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