Kevin Yu

Mathias Unberath, Kevin Yu, Roghayeh Barmaki et al.

Optical see-though head-mounted displays (OST HMDs) are one of the key technologies for merging virtual objects and physical scenes to provide an immersive mixed reality (MR) environment to its user. A fundamental limitation of HMDs is, that the user itself cannot be augmented conveniently as, in casual posture, only the distal upper extremities are within the field of view of the HMD. Consequently, most MR applications that are centered around the user, such as virtual dressing rooms or learning of body movements, cannot be realized with HMDs. In this paper, we propose a novel concept and prototype system that combines OST HMDs and physical mirrors to enable self-augmentation and provide an immersive MR environment centered around the user. Our system, to the best of our knowledge the first of its kind, estimates the user's pose in the virtual image generated by the mirror using an RGBD camera attached to the HMD and anchors virtual objects to the reflection rather than the user directly. We evaluate our system quantitatively with respect to calibration accuracy and infrared signal degradation effects due to the mirror, and show its potential in applications where large mirrors are already an integral part of the facility. Particularly, we demonstrate its use for virtual fitting rooms, gaming applications, anatomy learning, and personal fitness. In contrast to competing devices such as LCD-equipped smart mirrors, the proposed system consists of only an HMD with RGBD camera and, thus, does not require a prepared environment making it very flexible and generic. In future work, we will aim to investigate how the system can be optimally used for physical rehabilitation and personal training as a promising application.

Zhang Guo, Kevin Yu, Rebecca Pearlman et al.

The analysis of the collaborative learning process is one of the growing fields of education research, which has many different analytic solutions. In this paper, we provided a new solution to improve automated collaborative learning analyses using deep neural networks. Instead of using self-reported questionnaires, which are subject to bias and noise, we automatically extract group-working information by object recognition results using Mask R-CNN method. This process is based on detecting the people and other objects from pictures and video clips of the collaborative learning process, then evaluate the mobile learning performance using the collaborative indicators. We tested our approach to automatically evaluate the group-work collaboration in a controlled study of thirty-three dyads while performing an anatomy body painting intervention. The results indicate that our approach recognizes the differences of collaborations among teams of treatment and control groups in the case study. This work introduces new methods for automated quality prediction of collaborations among human-human interactions using computer vision techniques.

Sebastian Andress, Alex Johnson, Mathias Unberath et al.

Fluoroscopic X-ray guidance is a cornerstone for percutaneous orthopaedic surgical procedures. However, two-dimensional observations of the three-dimensional anatomy suffer from the effects of projective simplification. Consequently, many X-ray images from various orientations need to be acquired for the surgeon to accurately assess the spatial relations between the patient's anatomy and the surgical tools. In this paper, we present an on-the-fly surgical support system that provides guidance using augmented reality and can be used in quasi-unprepared operating rooms. The proposed system builds upon a multi-modality marker and simultaneous localization and mapping technique to co-calibrate an optical see-through head mounted display to a C-arm fluoroscopy system. Then, annotations on the 2D X-ray images can be rendered as virtual objects in 3D providing surgical guidance. We quantitatively evaluate the components of the proposed system, and finally, design a feasibility study on a semi-anthropomorphic phantom. The accuracy of our system was comparable to the traditional image-guided technique while substantially reducing the number of acquired X-ray images as well as procedure time. Our promising results encourage further research on the interaction between virtual and real objects, that we believe will directly benefit the proposed method. Further, we would like to explore the capabilities of our on-the-fly augmented reality support system in a larger study directed towards common orthopaedic interventions.

Long Qian, Mathias Unberath, Kevin Yu et al.

Purpose: Image guidance is crucial for the success of many interventions. Images are displayed on designated monitors that cannot be positioned optimally due to sterility and spatial constraints. This indirect visualization causes potential occlusion, hinders hand-eye coordination, leads to increased procedure duration and surgeon load. Methods: We propose a virtual monitor system that displays medical images in a mixed reality visualization using optical see-through head-mounted displays. The system streams high-resolution medical images from any modality to the head-mounted display in real-time that are blended with the surgical site. It allows for mixed reality visualization of images in head-, world-, or body-anchored mode and can thus be adapted to specific procedural needs. Results: For typical image sizes, the proposed system exhibits an average end-to-end delay and refresh rate of 214 +- 30 ms and 41:4 +- 32:0 Hz, respectively. Conclusions: The proposed virtual monitor system is capable of real-time mixed reality visualization of medical images. In future, we seek to conduct first pre-clinical studies to quantitatively assess the impact of the system on standard image guided procedures.