Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring

The widespread adoption of wearables, extended reality, and metaverses has accelerated the diverse configurations of remote collaboration and telementoring systems. This paper explores the opportunities and challenges of interactive telementoring, especially for wearers of smartglasses. In particular, recent relevant studies are reviewed to derive the needs and trends of telementoring technology. Based on this analysis, we define what can be integrated into smartglass-enabled interactive telementoring. To further illustrate this type of special use case for telementoring, we present five illustrative and descriptive scenarios. We expect our specialized use case to support various telementoring applications beyond medical and surgical telementoring, while harmoniously fostering cooperation using the smart devices of mentors and mentees at different scales for collocated, distributed, and remote collaboration

A Non-Touchscreen Tactile Wearable Interface as an Alternative to Touchscreen-Based Wearable Devices

Current consumer wearable devices such as smartwatches mostly rely on touchscreen-based user interfaces. Even though touch-based user interfaces help smartphone users quickly adapt to wearable devices with touchscreens, there exist several limitations. In this paper, we propose a non-touchscreen tactile wearable interface as an alternative to touchscreens on wearable devices. We designed and implemented a joystick-integrated smartwatch prototype to demonstrate our non-touchscreen tactile wearable interface. We iteratively improved and updated our prototype to improve and polish interaction ideas and prototype integration. To show feasibility of our approach, we compared and contrasted form factors of our prototype against the latest nine commercial smartwatches in terms of their dimensions. We also show response time and accuracy of our wearable interface to discuss our rationale for an alternative and usable wearable UI. With the proposed tactile wearable user interface, we believe our approach may serve as a cohesive single interaction device to enable various cross-device interaction scenarios and applications

Cross-Device Computation Coordination for Mobile Collocated Interactions with Wearables

Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices should communicate collectively to share computation burdens and stay energy-efficient. In this paper, we present a cross-device computation coordination method for scenarios of mobile collocated interactions with wearables. We formally define a cross-device computation coordination problem and propose a method for solving this problem. Lastly, we demonstrate the feasibility of our approach through experiments and exemplar cases using 12 commercial Android devices with varying computation capabilities

An All-in-One Vehicle Type and License Plate Recognition System Using YOLOv4

In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets. These use cases can be found in a variety of contexts and locations. It is critical to collect relevant data from the location where the application will be deployed. In this paper, we propose an integrated vehicle type and license plate recognition system using YOLOv4, which consists of vehicle type detection, license plate detection, and license plate character detection to better support the context of Korean vehicles in multilane highway and urban environments. Using our dataset of one to four multilane images, our system detected six vehicle classes and license plates with mAP of 98.0%, 94.0%, 97.1%, and 84.6%, respectively. On our dataset and a publicly available open dataset, our system demonstrated mAP of 99.3% and 99.4% for the detected license plates, respectively. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. We believe that our system can be used in densely populated regions to address the high demands for enhanced visual sensitivity in smart cities and Internet-of-Things

Integration of Extended Reality and a High-Fidelity Simulator in Team-Based Simulations for Emergency Scenarios

Wearable devices such as smart glasses are considered promising assistive tools for information exchange in healthcare settings. We aimed to evaluate the usability and feasibility of smart glasses for team-based simulations constructed using a high-fidelity simulator. Two scenarios of patients with arrhythmia were developed to establish a procedure for interprofessional interactions via smart glasses using 15-h simulation training. Three to four participants formed a team and played the roles of remote supporter or bed-side trainee with smart glasses. Usability, attitudes towards the interprofessional health care team and learning satisfaction were assessed. Using a 5-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree), 31 participants reported that the smart glasses were easy to use (3.61 ± 0.95), that they felt confident during use (3.90 ± 0.87), and that that responded positively to long-term use (3.26 ± 0.89) and low levels of physical discomfort (1.96 ± 1.06). The learning satisfaction was high (4.65 ± 0.55), and most (84%) participants found the experience favorable. Key challenges included an unstable internet connection, poor resolution and display, and physical discomfort while using the smart glasses with accessories. We determined the feasibility and acceptability of smart glasses for interprofessional interactions within a team-based simulation. Participants responded favorably toward a smart glass-based simulation learning environment that would be applicable in clinical settings

A Case Study on Iteratively Assessing and Enhancing Wearable User Interface Prototypes

Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era. To deal with a series of rapidly developed wearable prototypes, a well-structured strategy is required to assess the prototypes at various development stages. In this paper, we first design and develop variants of advanced wearable user interface prototypes, including joystick-embedded, potentiometer-embedded, motion-gesture and contactless infrared user interfaces for rapidly assessing hands-on user experience of potential futuristic user interfaces. To achieve this goal systematically, we propose a conceptual test framework and present a case study of using the proposed framework in an iterative cyclic process to prototype, test, analyze, and refine the wearable user interface prototypes. We attempt to improve the usability of the user interface prototypes by integrating initial user feedback into the leading phase of the test framework. In the following phase of the test framework, we track signs of improvements through the overall results of usability assessments, task workload assessments and user experience evaluation of the prototypes. The presented comprehensive and in-depth case study demonstrates that the iterative approach employed by the test framework was effective in assessing and enhancing the prototypes, as well as gaining insights on potential applications and establishing practical guidelines for effective and usable wearable user interface development