Using the Concept of Augmented Reality as a Vehicle for Transcending the Desktop Tarpit

of technical substrates for mixed environments: augmenting the user, the physical object and the environment. These strategies describe the technical locus of the interface assuming the analytical separation of function and interaction in the computer artefact. However, the way we have employed the augmented reality principles goes far beyond the original purpose, as we have used them as a tool for divergent thinking, a kind of metaphor or springboard (refs??). We abstracted defining features from the three directions in augmented reality interfaces and applied them in the different technical settings. Subsequently, the principles were further investigated through future scenarios of PDA support for wastewater treatment work built on the augmented reality classification transformed to small mobile interfaces. We developed future scenarios for wastewater treatment work with PDA applications developed by using the technical classification of augmented reality interfaces as a thinking to

Benchmark movement data set for trust assessment in human robot collaboration

In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire

Benchmark EEG data set for trust assessment for interactions with social robots

The data collection consisted of a game interaction with a small humanoid EZ-robot. The robot explains a word to the participant either through movements depicting the concept or by verbal description. Depending on their performance, participants could "earn" or loose candy as remuneration for their participation. The dataset comprises EEG (Electroencephalography) recordings from 21 participants, gathered using Emotiv headsets. Each participant's EEG data includes timestamps and measurements from 14 sensors placed across different regions of the scalp. The sensor labels in the header are as follows: EEG.AF3, EEG.F7, EEG.F3, EEG.FC5, EEG.T7, EEG.P7, EEG.O1, EEG.O2, EEG.P8, EEG.T8, EEG.FC6, EEG.F4, EEG.F8, EEG.AF4, and Time

Machine learning in general practice: scoping review of administrative task support and automation

Abstract Background Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. Conclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done

Machine learning in general practice: scoping review of administrative task support and automation

Abstract Background Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. Conclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done

Dataset for "A Novel Neural Network Architecture with Applications to 3D Animation and Interaction in Virtual Reality"

This is the dataset for the doctoral thesis "A Novel Neural Network Architecture with Applications to 3D Animation and Interaction in Virtual Reality" by Javier de la Dehesa Cueto-Felgueroso. See the original document for details. The dataset is structured in three parts. The files `gfnn_code.zip` and `gfnn_data.zip` contain the code and data for the experiments with grid-functioned neural networks discussed in chapter 3 of the thesis. The files `quadruped_code.zip` and `quadruped_data.zip` contain the code and data for the quadruped locomotion experiments and user study discussed in chapter 4. The files `framework_code.zip` and `framework_data.zip` contain the code and data for the human-character interaction framework experiments and user studies discussed in chapter 5. Each pair of files should be decompressed in the same directory, but separate from the other parts. Further details and instructions for each of the parts can be found within the corresponding compressed files

Dataset for "Touché: Data-Driven Interactive Sword Fighting in Virtual Reality"

This is the data repository for the paper "Touché: Data-Driven Interactive Sword Fighting in Virtual Reality" by Javier Dehesa, Andrew Vidler, Christof Lutteroth and Julian Padget, presented at CHI 2020 conference in Honolulu, HI, USA. See the publication for details. The archives gesture_recognition_data.zip and gesture_recognition_code.zip contain respectively the data and code for the gesture recognition component. Similarly, the archives animation_data.zip and animation_code.zip contain respectively the data and code for the animation component. Instructions about how to use these are provided within them. The archive user_studies.zip contains information about our user studies. The file questionnaire_study.jasp and interactive_study.jasp contain the data and analysis of the questionnaire and interactive studies respectively. They can be consulted with the open source tool JASP (https://jasp-stats.org/). The video questionnaire_conditions.mp4 shows the full videos used as the three conditions for the questionnaire study

Dataset for "A Novel Neural Network Architecture with Applications to 3D Animation and Interaction in Virtual Reality"

This is the dataset for the doctoral thesis "A Novel Neural Network Architecture with Applications to 3D Animation and Interaction in Virtual Reality" by Javier de la Dehesa Cueto-Felgueroso. See the original document for details. The dataset is structured in three parts. The files `gfnn_code.zip` and `gfnn_data.zip` contain the code and data for the experiments with grid-functioned neural networks discussed in chapter 3 of the thesis. The files `quadruped_code.zip` and `quadruped_data.zip` contain the code and data for the quadruped locomotion experiments and user study discussed in chapter 4. The files `framework_code.zip` and `framework_data.zip` contain the code and data for the human-character interaction framework experiments and user studies discussed in chapter 5. Each pair of files should be decompressed in the same directory, but separate from the other parts. Further details and instructions for each of the parts can be found within the corresponding compressed files