A User Study for the Evaluation of Adaptive Interaction Systems for Inclusive Industrial Workplaces

AbstractVirtual and augmented reality allows the utilization of natural user interfaces, such as realistic finger interaction, even for purposes that were previously dominated by the WIMP paradigm. This new form of interaction is particularly suitable for applications involving manipulation tasks in 3D space, such as CAD assembly modeling. The objective of this paper is to evaluate the suitability of natural interaction for CAD assembly modeling in virtual reality. An advantage of the natural interaction compared to the conventional operation by computer mouse would indicate development potential for user interfaces of current CAD applications. Our approach bases on two main elements. Firstly, a novel natural user interface for realistic finger interaction enables the user to interact with virtual objects similar to physical ones. Secondly, an algorithm automatically detects constraints between CAD components based solely on their geometry and spatial location. In order to prove the usability of the natural CAD assembly modeling approach in comparison with the assembly procedure in current WIMP operated CAD software, we present a comparative user study. Results show that the VR method including natural finger interaction significantly outperforms the desktop-based CAD application in terms of efficiency and ease of use.

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Back to reality: transition techniques from short HMD-based virtual experiences to the physical world

Multimedia Tools and Applications ◽

10.1007/s11042-021-11317-w ◽

2021 ◽

Author(s):

Robin Horst ◽

Ramtin Naraghi-Taghi-Off ◽

Linda Rau ◽

Ralf Dörner

Keyword(s):

Virtual Reality ◽

Virtual World ◽

Visual Cues ◽

User Study ◽

Physical World ◽

Direct Communication ◽

Common Presentation ◽

Presentation Media ◽

Virtual Experiences ◽

Short Times

AbstractEvery Virtual Reality (VR) experience has to end at some point. While there already exist concepts to design transitions for users to enter a virtual world, their return from the physical world should be considered, as well, as it is a part of the overall VR experience. We call the latter outro-transitions. In contrast to offboarding of VR experiences, that takes place after taking off VR hardware (e.g., HMDs), outro-transitions are still part of the immersive experience. Such transitions occur more frequently when VR is experienced periodically and for only short times. One example where transition techniques are necessary is in an auditorium where the audience has individual VR headsets available, for example, in a presentation using PowerPoint slides together with brief VR experiences sprinkled between the slides. The audience must put on and take off HMDs frequently every time they switch from common presentation media to VR and back. In a such a one-to-many VR scenario, it is challenging for presenters to explore the process of multiple people coming back from the virtual to the physical world at once. Direct communication may be constrained while VR users are wearing an HMD. Presenters need a tool to indicate them to stop the VR session and switch back to the slide presentation. Virtual visual cues can help presenters or other external entities (e.g., automated/scripted events) to request VR users to end a VR session. Such transitions become part of the overall experience of the audience and thus must be considered. This paper explores visual cues as outro-transitions from a virtual world back to the physical world and their utility to enable presenters to request VR users to end a VR session. We propose and investigate eight transition techniques. We focus on their usage in short consecutive VR experiences and include both established and novel techniques. The transition techniques are evaluated within a user study to draw conclusions on the effects of outro-transitions on the overall experience and presence of participants. We also take into account how long an outro-transition may take and how comfortable our participants perceived the proposed techniques. The study points out that they preferred non-interactive outro-transitions over interactive ones, except for a transition that allowed VR users to communicate with presenters. Furthermore, we explore the presenter-VR user relation within a presentation scenario that uses short VR experiences. The study indicates involving presenters that can stop a VR session was not only negligible but preferred by our participants.

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Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

Applied Sciences ◽

10.3390/app11136047 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6047

Author(s):

Soheil Rezaee ◽

Abolghasem Sadeghi-Niaraki ◽

Maryam Shakeri ◽

Soo-Mi Choi

Keyword(s):

Big Data ◽

Augmented Reality ◽

User Study ◽

Spatial Information ◽

Ease Of Use ◽

Tourist Attraction ◽

Gender And Education ◽

Time Distance ◽

Data Source ◽

The Right

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

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Enhancing spatial perception through sound: mapping human movements into MIDI

Multimedia Tools and Applications ◽

10.1007/s11042-021-11077-7 ◽

2021 ◽

Author(s):

Bernardo Breve ◽

Stefano Cirillo ◽

Mariano Cuofano ◽

Domenico Desiato

Keyword(s):

Real Time ◽

User Study ◽

Spatial Perception ◽

Human Movements ◽

Different Types ◽

Real Time Tracking ◽

People’S Perceptions ◽

Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

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Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽

10.1515/icom-2021-0002 ◽

2021 ◽

Vol 20 (1) ◽

pp. 19-32

Author(s):

Daniel Buschek ◽

Charlotte Anlauff ◽

Florian Lachner

Keyword(s):

Machine Learning ◽

Development Process ◽

User Study ◽

Concept Development ◽

Lessons Learned ◽

Design Tool ◽

Learning Tools ◽

Interface Elements ◽

Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

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Bringing Things Closer: Enhancing Low-Vision Interaction Experience with Office Productivity Applications

Proceedings of the ACM on Human-Computer Interaction ◽

10.1145/3457144 ◽

2021 ◽

Vol 5 (EICS) ◽

pp. 1-18

Author(s):

Hae-Na Lee ◽

Vikas Ashok ◽

IV Ramakrishnan

Keyword(s):

Assistive Technology ◽

User Study ◽

State Of The Art ◽

Low Vision ◽

Spatial Separation ◽

Usability Study ◽

Presentation Software ◽

Screen Magnifier ◽

Word Processors ◽

Grid Layout

Many people with low vision rely on screen-magnifier assistive technology to interact with productivity applications such as word processors, spreadsheets, and presentation software. Despite the importance of these applications, little is known about their usability with respect to low-vision screen-magnifier users. To fill this knowledge gap, we conducted a usability study with 10 low-vision participants having different eye conditions. In this study, we observed that most usability issues were predominantly due to high spatial separation between main edit area and command ribbons on the screen, as well as the wide span grid-layout of command ribbons; these two GUI aspects did not gel with the screen-magnifier interface due to lack of instantaneous WYSIWYG (What You See Is What You Get) feedback after applying commands, given that the participants could only view a portion of the screen at any time. Informed by the study findings, we developed MagPro, an augmentation to productivity applications, which significantly improves usability by not only bringing application commands as close as possible to the user's current viewport focus, but also enabling easy and straightforward exploration of these commands using simple mouse actions. A user study with nine participants revealed that MagPro significantly reduced the time and workload to do routine command-access tasks, compared to using the state-of-the-art screen magnifier.

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Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽

10.3390/math8122258 ◽

2020 ◽

Vol 8 (12) ◽

pp. 2258

Author(s):

Madhab Raj Joshi ◽

Lewis Nkenyereye ◽

Gyanendra Prasad Joshi ◽

S. M. Riazul Islam ◽

Mohammad Abdullah-Al-Wadud ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

User Study ◽

Mean Squared Error ◽

Color Image ◽

Machine Learning Techniques ◽

Global Features ◽

Black And White ◽

Historical Images ◽

Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

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Improved Mutual Understanding for Human-Robot Collaboration: Combining Human-Aware Motion Planning with Haptic Feedback Devices for Communicating Planned Trajectory

Sensors ◽

10.3390/s21113673 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3673

Author(s):

Stefan Grushko ◽

Aleš Vysocký ◽

Petr Oščádal ◽

Michal Vocetka ◽

Petr Novák ◽

...

Keyword(s):

User Study ◽

Haptic Feedback ◽

Fundamental Aspect ◽

Task Completion ◽

Good Efficiency ◽

Notification System ◽

Movement Data ◽

Novel Approach ◽

Machine Interface ◽

High Degree

In a collaborative scenario, the communication between humans and robots is a fundamental aspect to achieve good efficiency and ergonomics in the task execution. A lot of research has been made related to enabling a robot system to understand and predict human behaviour, allowing the robot to adapt its motion to avoid collisions with human workers. Assuming the production task has a high degree of variability, the robot’s movements can be difficult to predict, leading to a feeling of anxiety in the worker when the robot changes its trajectory and approaches since the worker has no information about the planned movement of the robot. Additionally, without information about the robot’s movement, the human worker cannot effectively plan own activity without forcing the robot to constantly replan its movement. We propose a novel approach to communicating the robot’s intentions to a human worker. The improvement to the collaboration is presented by introducing haptic feedback devices, whose task is to notify the human worker about the currently planned robot’s trajectory and changes in its status. In order to verify the effectiveness of the developed human-machine interface in the conditions of a shared collaborative workspace, a user study was designed and conducted among 16 participants, whose objective was to accurately recognise the goal position of the robot during its movement. Data collected during the experiment included both objective and subjective parameters. Statistically significant results of the experiment indicated that all the participants could improve their task completion time by over 45% and generally were more subjectively satisfied when completing the task with equipped haptic feedback devices. The results also suggest the usefulness of the developed notification system since it improved users’ awareness about the motion plan of the robot.

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