We present Y-zipper, a novel three-sided 3D-printed zipper structure that enables three flexible strips to interlock and transform into a rigid rod-like form. Building on this flex–rigid transition mechanism, we further design a specialized slider to achieve rapid and reversible zipping interactions. This slider serves as the basis for three actuation methods—manual, dynamic mechanical, and static mechanical—which enable both remote control and automated closure and release. In addition, Y-zipper provides four motion primitives: straight, bend, coil, and screw, whose combinations extend the flex–rigid transition mechanism to spatial curve structures. To support customization, we develop a computational design tool that automatically generates zipper geometry based on input primitives, unfolds the structure for 3D printing, and embeds both teeth and compliant bridges. Controlled experiments evaluate its mechanical properties, repeatability, and actuation speed, demonstrating robustness and reliability. Finally, we showcase a series of functional prototypes, including a medical wrist brace, a kinetic art installation, and a rapidly deployable tent structure.

We propose a method to fabricate objects composed of 3D printed flattened pieces with integrated zipper-like structures. The object is manually assembled into a 3D shape by connecting the zipper components. By employing a zipper design that allows for angle-independent connections between patches, our method enables both the surface and zipper components to be printed in the same orientation, resulting in high-quality reconstruction of the input model with a faster 3D printing process that wastes less material. We implement a fully automated pipeline that takes a 3D model as input, converts it into developable patches, generates the zipper structures, and flattens them for subsequent 3D printing. We demonstrate that our approach significantly reduces the fabrication time and support material consumption. We also present application examples that highlight the versatility of our method.

Figure 1: FlexEar-Tips overview and application examples.A: FlexEar-Tips, an ear tip system for hearables that allows shape adjustment through pressure control, B: user wearing the device, C: haptics features during gameplay, D: music listening experience enhancement and notification features, E: an image of ear tip status transitions.

Smart rings are used for contactless payment, smart lock operation, and health monitoring. For applications such as electronic payment and unlocking smart locks, the implementation of a user authentication system in smart rings is essential; however, some challenges remain. Fingerprint authentication is sensitive to fingertip conditions, while face authentication faces difficulties with miniaturization, power efficiency, and privacy. This study proposes ScanRing, a hybrid authentication system using a distance sensor and an IMU sensor in a smart ring. By moving the ring device laterally in front of the face, the distance sensor captures facial structure data, while the IMU sensor records the user’s motion characteristics. These combined datasets enable robust user authentication without relying on cameras, which enhances privacy while supporting a compact and power-efficient design. A user study (N = 30) demonstrated that ScanRing achieved an average authentication accuracy of 98.41 \(\%\) under stable conditions.

Hearables are highly functional earphone-type wearables; however, existing input methods using stand-alone hearables are limited in the number of commands, and there is a need to extend device operation through hand gestures. In previous research on hearables for hand input, user understanding and gesture recognition systems have been developed. However, in the realm of user understanding, investigation concerning hand input with hearables remains incomplete, and existing recognition systems have not demonstrated proficiency in discerning user-defined gestures. In this study, we conducted a gesture elicitation study (GES) assuming hand input using hearables under six conditions (three interaction areas x two device shapes). Then, we extracted ear-level gestures that the device's built-in IMU sensor could recognize from the user-defined gestures and investigated the recognition performance. The results of sitting experiments showed that the gesture recognition rate for in-ear devices was 91.0% and that for ear-hook devices was 74.7%.

Embedding technology into day-to-day wearables and creating smart devices such as smartwatches and smart-glasses has been a growing area of interest. In this paper, we explore the interaction around face masks, a common accessory worn by many to prevent the spread of infectious diseases. Particularly, we propose a method of using the straps of a face mask as an input medium. We identified a set of plausible gestures on mask straps through an elicitation study (N = 20), in which the participants proposed different gestures for a given referent. We then developed a prototype to identify the gestures performed on the mask straps and present the recognition accuracy from a user study with eight participants. Our results show the system achieves 93.07% classification accuracy for 12 gestures.

Human activities can introduce variations in various environmental cues, such as light and sound, which can serve as inputs for interfaces. However, one often overlooked aspect is the airflow variation caused by these activities, which presents challenges in detection and utilization due to its intangible nature. In this paper, we have unveiled an approach using mist to capture invisible airflow variations, rendering them detectable by Time-of-Flight (ToF) sensors. We investigate the capability of this sensing technique under different types of mist or smoke, as well as the impact of airflow speed. To illustrate the feasibility of this concept, we created a prototype using a humidifier and demonstrated its capability to recognize motions. On this basis, we introduce potential applications, discuss inherent limitations, and provide design lessons grounded in mist-based airflow sensing.

A CAPTCHA is a technique that is used to prevent automated programs from being able to acquire free e-mail or online service accounts. However, as many researchers have already reported, conventional CAPTCHAs could be overcome by state-of-the-art malwares since the capabilities of computers are approaching those of humans. Therefore, CAPTCHAs should be based on even more advanced human-cognitive-processing abilities. In addition, it is also important to keep in mind that answering CAPTCHAs is an added annoyance for users, who feel troublesome to prove that they are humans at every Web accesses. So, CAPTCHAs should be enjoyable for users. To cope with these issues, we have focused on the human ability to understand humor which is considered one of the most advanced human cognitive processing abilities, and already proposed the concept of a new type of Turing test that uses four-panel cartoons, which would make CAPTCHAs fun and enjoyable. So in this paper, we carry out experimental studies to confirm the usability of the proposed CAPTCHA.

CAPTCHA is a technique that is used to prevent automatic programs from being able to acquire free e-mail or online service accounts. However, as many researchers have already reported, conventional CAPTCHA could be overcome by state-of-the-art malware since the capabilities of computers are approaching those of humans. Therefore, CAPTCHA should be based on even more advanced human-cognitive-processing abilities. We propose using the human ability of recognizing “strangeness” to achieve a new CAPTCHA. This paper focuses on strangeness in machine-translated sentences as an example, and proposes CAPTCHA using Strangeness in Sentences (SS-CAPTCHA), which detects malware by checking if users can distinguish natural sentences created by humans from machine-translated sentences. We discuss possible threats to SS-CAPTCHA and countermeasures against these threats. We also carried out basic experiments to confirm its usability by human users.

This paper describes how to make use of human visual capability to improve information security. Here in this paper, two pilot studies are shown; a content protection scheme with image tainting, and a user authentication scheme with unclear images.

A user authentication system using "unclear images" as pass-images has been proposed, in which only legitimate users can understand their meaning by viewing the original images corresponding to these unclear pass-images, which are meaningless to unauthorized users. Hence, it is difficult for attackers to memorize them, even though they may have observed authentication trials by legitimate users. This paper reports another advantage of the user-authentication system using unclear images: their adaptation enables decoy images to be automatically generated, which are displayed along with the pass-images in the authentication window. Here, we explore various methods of automatically generating decoy images.

A user authentication system using "unclear images" as pass-images has been proposed, in which only legitimate users can understand their meaning by viewing the original images corresponding to the unclear pass-images. These unclear images are meaningless to unauthorized users. Hence, it is difficult for attackers to memorize these unclear pass-images, even though they may have observed legitimate users' authentication trials. However, as long as the same pass-images are always used, it would not be completely impossible for unauthorized users to memorize unclear-images. Therefore, this paper discusses enhancement to the authentication system by permitting users to memorize a number of pass-images and using a different subset of pass-images at all authentication trials. To mitigate the burden imposed by memorizing additional pass-images in the enhanced system, we modified it by extracting a certain number of images from a video sequence. The pass-images were obtained by making the extracted images unclear. As only legitimate users were allowed to view the video sequence in their registration phase, only they could understand what the unclear pass-images meant. Thus, the unclear pass-images become easy for legitimate users to memorize. We verified the effectiveness of the enhanced system through comparative experiments.