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Abstract Augmented reality (AR) enhances the user’s perception of the real environment by superimposing virtual images generated by computers. These virtual images provide additional visual information that complements the real-world view. AR systems are rapidly gaining popularity in various manufacturing fields such as training, maintenance, assembly, and robot programming. In some AR applications, it is crucial for the invisible virtual environment to be precisely aligned with the physical environment to ensure that human users can accurately perceive the virtual augmentation in conjunction with their real surroundings. The process of achieving this accurate alignment is known as calibration. During some robotics applications using AR, we observed instances of misalignment in the visual representation within the designated workspace. This misalignment can potentially impact the accuracy of the robot’s operations during the task. Based on the previous research on AR-assisted robot programming systems, this work investigates the sources of misalignment errors and presents a simple and efficient calibration procedure to reduce the misalignment accuracy in general video see-through AR systems. To accurately superimpose virtual information onto the real environment, it is necessary to identify the sources and propagation of errors. In this work, we outline the linear transformation and projection of each point from the virtual world space to the virtual screen coordinates. An offline calibration method is introduced to determine the offset matrix from the head-mounted display (HMD) to the camera, and experiments are conducted to validate the improvement achieved through the calibration process. 

Overcoming challenges and transitioning from school to work is particularly problematic for individuals who are deaf or hard of hearing, presenting significant issues for both the labor market and vocational training institutions. Due to the lack of research addressing the career maturity and distinctive obstacles faced by this population, this paper endeavors to investigate performance disparities within the machining field. The specific focus is on assessing whether hearing loss may impact students' machining performance. Considering the essential human capabilities for perception in machining, especially in industrial settings, encompass a range of faculties including visualization, hearing, and tactile senses. Thus, addressing concerns related to accommodating individuals with disabilities is important, prompting inquiries into optimizing training programs and quantifying potential disparities in learning or schooling outcomes, behavioral patterns, and overall performance in future careers. The conducted studies involved multiple participants, including hearing, deaf, and hard-of-hearing students with various machining training backgrounds. The investigation will delve into data concerning the qualities of manual machining outputs and the subject’s self-rating feedback. The outcomes from this study are expected not only to allow to obtain more insights into human behavior in machining operations, but also to identify key differences between machinist trainees who exhibit no underlying hearing problems and ones who are deaf/hard of hearing. The findings of this work provide valuable takeaways concerning machinists with hearing loss, revealing little to no effect of hearing loss on trainee performance, alleviating concerns about potential performance weaknesses. The outcomes from this study have shown that trainee experience seems to relate directly to machining proficiency, regardless of hearing loss. 

Abstract This paper aims to present a potential cybersecurity risk existing in mixed reality (MR)-based smart manufacturing applications that decipher digital passwords through a single RGB camera to capture the user’s mid-air gestures. We first created a test bed, which is an MR-based smart factory management system consisting of mid-air gesture-based user interfaces (UIs) on a video see-through MR head-mounted display. To interact with UIs and input information, the user’s hand movements and gestures are tracked by the MR system. We setup the experiment to be the estimation of the password input by users through mid-air hand gestures on a virtual numeric keypad. To achieve this goal, we developed a lightweight machine learning-based hand position tracking and gesture recognition method. This method takes either video streaming or recorded video clips (taken by a single RGB camera in front of the user) as input, where the videos record the users’ hand movements and gestures but not the virtual UIs. With the assumption of the known size, position, and layout of the keypad, the machine learning method estimates the password through hand gesture recognition and finger position detection. The evaluation result indicates the effectiveness of the proposed method, with a high accuracy of 97.03%, 94.06%, and 83.83% for 2-digit, 4-digit, and 6-digit passwords, respectively, using real-time video streaming as input with known length condition. Under the unknown length condition, the proposed method reaches 85.50%, 76.15%, and 77.89% accuracy for 2-digit, 4-digit, and 6-digit passwords, respectively. 

Current hand wearables have limited customizability, they are loose-fit to an individual's hand and lack comfort. The main barrier in customizing hand wearables is the geometric complexity and size variation in hands. Moreover, there are different functions that the users can be looking for; some may only want to detect hand's motion or orientation; others may be interested in tracking their vital signs. Current wearables usually fit multiple functions and are designed for a universal user with none or limited customization. There are no specialized tools that facilitate the creation of customized hand wearables for varying hand sizes and provide different functionalities. We envision an emerging generation of customizable hand wearables that supports hand differences and promotes hand exploration with additional functionality. We introduce FabHandWear, a novel system that allows end-to-end design and fabrication of customized functional self-contained hand wearables. FabHandWear is designed to work with off-the-shelf electronics, with the ability to connect them automatically and generate a printable pattern for fabrication. We validate our system by using illustrative applications, a durability test, and an empirical user evaluation. Overall, FabHandWear offers the freedom to create customized, functional, and manufacturable hand wearables.