scholarly journals Wearable Augmented Reality Platform for Aiding Complex 3D Trajectory Tracing

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1612 ◽  
Author(s):  
Sara Condino ◽  
Benish Fida ◽  
Marina Carbone ◽  
Laura Cercenelli ◽  
Giovanni Badiali ◽  
...  
Keyword(s):  
Augmented Reality ◽  
High Precision ◽  
Ad Hoc ◽  
User Study ◽  
Peripersonal Space ◽  
Planar Structures ◽  
3D Printed ◽  
Quantitative Results ◽  
Manual Tasks ◽  
Wearable Augmented Reality

Augmented reality (AR) Head-Mounted Displays (HMDs) are emerging as the most efficient output medium to support manual tasks performed under direct vision. Despite that, technological and human-factor limitations still hinder their routine use for aiding high-precision manual tasks in the peripersonal space. To overcome such limitations, in this work, we show the results of a user study aimed to validate qualitatively and quantitatively a recently developed AR platform specifically conceived for guiding complex 3D trajectory tracing tasks. The AR platform comprises a new-concept AR video see-through (VST) HMD and a dedicated software framework for the effective deployment of the AR application. In the experiments, the subjects were asked to perform 3D trajectory tracing tasks on 3D-printed replica of planar structures or more elaborated bony anatomies. The accuracy of the trajectories traced by the subjects was evaluated by using templates designed ad hoc to match the surface of the phantoms. The quantitative results suggest that the AR platform could be used to guide high-precision tasks: on average more than 94% of the traced trajectories stayed within an error margin lower than 1 mm. The results confirm that the proposed AR platform will boost the profitable adoption of AR HMDs to guide high precision manual tasks in the peripersonal space.

scholarly journals Optical See-Through Head-Mounted Displays With Short Focal Distance: Conditions for Mitigating Parallax-Related Registration Error

2020 ◽  
Vol 7 ◽  
Author(s):  
Fabrizio Cutolo ◽  
Nadia Cattari ◽  
Umberto Fontana ◽  
Vincenzo Ferrari
Keyword(s):  
High Precision ◽  
Peripersonal Space ◽  
Calibration Procedure ◽  
Depth Range ◽  
Focal Distance ◽  
Registration Error ◽  
Head Mounted Displays ◽  
Beneficial Impact ◽  
Manual Tasks ◽  
Application Fields

Optical see-through (OST) augmented reality head-mounted displays are quickly emerging as a key asset in several application fields but their ability to profitably assist high precision activities in the peripersonal space is still sub-optimal due to the calibration procedure required to properly model the user's viewpoint through the see-through display. In this work, we demonstrate the beneficial impact, on the parallax-related AR misregistration, of the use of optical see-through displays whose optical engines collimate the computer-generated image at a depth close to the fixation point of the user in the peripersonal space. To estimate the projection parameters of the OST display for a generic viewpoint position, our strategy relies on a dedicated parameterization of the virtual rendering camera based on a calibration routine that exploits photogrammetry techniques. We model the registration error due to the viewpoint shift and we validate it on an OST display with short focal distance. The results of the tests demonstrate that with our strategy the parallax-related registration error is submillimetric provided that the scene under observation stays within a suitable view volume that falls in a ±10 cm depth range around the focal plane of the display. This finding will pave the way to the development of new multi-focal models of OST HMDs specifically conceived to aid high-precision manual tasks in the peripersonal space.

scholarly journals In Situ Visualization for 3D Ultrasound-Guided Interventions with Augmented Reality Headset

2021 ◽  
Vol 8 (10) ◽  
pp. 131
Author(s):  
Nadia Cattari ◽  
Sara Condino ◽  
Fabrizio Cutolo ◽  
Mauro Ferrari ◽  
Vincenzo Ferrari
Keyword(s):  
Augmented Reality ◽  
Ultrasound Imaging ◽  
High Precision ◽  
Soft Tissues ◽  
Imaging System ◽  
3D Ultrasound ◽  
Integrated System ◽  
Head Mounted Display ◽  
Manual Tasks

Augmented Reality (AR) headsets have become the most ergonomic and efficient visualization devices to support complex manual tasks performed under direct vision. Their ability to provide hands-free interaction with the augmented scene makes them perfect for manual procedures such as surgery. This study demonstrates the reliability of an AR head-mounted display (HMD), conceived for surgical guidance, in navigating in-depth high-precision manual tasks guided by a 3D ultrasound imaging system. The integration between the AR visualization system and the ultrasound imaging system provides the surgeon with real-time intra-operative information on unexposed soft tissues that are spatially registered with the surrounding anatomic structures. The efficacy of the AR guiding system was quantitatively assessed with an in vitro study simulating a biopsy intervention aimed at determining the level of accuracy achievable. In the experiments, 10 subjects were asked to perform the biopsy on four spherical lesions of decreasing sizes (10, 7, 5, and 3 mm). The experimental results showed that 80% of the subjects were able to successfully perform the biopsy on the 5 mm lesion, with a 2.5 mm system accuracy. The results confirmed that the proposed integrated system can be used for navigation during in-depth high-precision manual tasks.

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

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%.

scholarly journals shARe-IT: Ad hoc Remote Troubleshooting through Augmented Reality

2021 ◽  
Author(s):  
Thomas Ludwig ◽  
Oliver Stickel ◽  
Peter Tolmie ◽  
Malte Sellmer
Keyword(s):  
Augmented Reality ◽  
Ad Hoc ◽  
Cooperative Work ◽  
Computer Supported Cooperative Work ◽  
Limiting Factors ◽  
Minor Role ◽  
Articulation Work ◽  
Remote Collaboration ◽  
Expertise Sharing ◽  
A Minor

Abstract10 years ago, Castellani et al. (Journal of Computer Supported Cooperative Work, vol. 18, no. 2–3, 2009, pp. 199–227, 2009) showed that using just an audio channel for remote troubleshooting can lead to a range of problems and already envisioned a future in which augmented reality (AR) could solve many of these issues. In the meantime, AR technologies have found their way into our everyday lives and using such technologies to support remote collaboration has been widely studied within the fields of Human-Computer Interaction and Computer-Supported Cooperative Work. In this paper, we contribute to this body of research by reporting on an extensive empirical study within a Fab Lab of troubleshooting and expertise sharing and the potential relevance of articulation work to their realization. Based on the findings of this study, we derived design challenges that led to an AR-based concept, implemented as a HoloLens application, called shARe-it. This application is designed to support remote troubleshooting and expertise sharing through different communication channels and AR-based interaction modalities. Early testing of the application revealed that novel interaction modalities such as AR-based markers and drawings play only a minor role in remote collaboration due to various limiting factors. Instead, the transmission of a shared view and especially arriving at a shared understanding of the situation as a prerequisite for articulation work continue to be the decisive factors in remote troubleshooting.

scholarly journals Multimodal Mixed Reality Impact on a Hand Guiding Task with a Holographic Cobot

2020 ◽  
Vol 4 (4) ◽  
pp. 78
Author(s):  
Andoni Rivera Pinto ◽  
Johan Kildal ◽  
Elena Lazkano
Keyword(s):  
Augmented Reality ◽  
Industrial Production ◽  
User Study ◽  
Haptic Feedback ◽  
Mixed Reality ◽  
Robot Arm ◽  
Pick And Place ◽  
Place Task ◽  
Guidance Technique ◽  
The Impact

In the context of industrial production, a worker that wants to program a robot using the hand-guidance technique needs that the robot is available to be programmed and not in operation. This means that production with that robot is stopped during that time. A way around this constraint is to perform the same manual guidance steps on a holographic representation of the digital twin of the robot, using augmented reality technologies. However, this presents the limitation of a lack of tangibility of the visual holograms that the user tries to grab. We present an interface in which some of the tangibility is provided through ultrasound-based mid-air haptics actuation. We report a user study that evaluates the impact that the presence of such haptic feedback may have on a pick-and-place task of the wrist of a holographic robot arm which we found to be beneficial.

scholarly journals Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

2021 ◽  
Author(s):  
Leonardo Tanzi ◽  
Pietro Piazzolla ◽  
Francesco Porpiglia ◽  
Enrico Vezzetti
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Augmented Reality ◽  
Ad Hoc ◽  
Geodesic Distance ◽  
Semantic Segmentation ◽  
Endoscopic Image ◽  
Operative Surgery ◽  
Endoscopic Videos

Abstract Purpose The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient’s organ with its 2D endoscopic image, to assist surgeons during the procedure. Methods This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. We used a dataset obtained from 5 endoscopic videos (A, B, C, D, E), selected and tagged by our team’s specialists. We then evaluated the most performing couple of segmentation architecture and neural network and tested the overlay performances. Results U-Net stood out as the most effecting architectures for segmentation. ResNet and MobileNet obtained similar Intersection over Unit (IoU) results but MobileNet was able to elaborate almost twice operations per seconds. This segmentation technique outperformed the results from the former work, obtaining an average IoU for the catheter of 0.894 (σ = 0.076) compared to 0.339 (σ = 0.195). This modifications lead to an improvement also in the 3D overlay performances, in particular in the Euclidean Distance between the predicted and actual model’s anchor point, from 12.569 (σ= 4.456) to 4.160 (σ = 1.448) and in the Geodesic Distance between the predicted and actual model’s rotations, from 0.266 (σ = 0.131) to 0.169 (σ = 0.073). Conclusion This work is a further step through the adoption of DL and AR in the surgery domain. In future works, we will overcome the limits of this approach and finally improve every step of the surgical procedure.

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