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.

Earphone-type wearable devices, also known as “hearables,” will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock's authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2–93.7%, 83.4–94.7%, and 85.9–90.0%.

We introduce EarHover, an innovative system that enables mid-air gesture input for hearables. Mid-air gesture input, which eliminates the need to touch the device and thus helps to keep hands and the device clean. However, existing mid-air gesture input methods for hearables have been limited to adding cameras or infrared sensors. By focusing on the sound leakage phenomenon unique to hearables, we have realized mid-air gesture recognition using a speaker and an external microphone that are highly compatible with hearables. The signal leaked to the outside of the device due to sound leakage can be measured by an external microphone, which detects the differences in reflection characteristics caused by the hand’s speed and shape during mid-air gestures. Among 27 types of gestures, we determined the seven suitable gestures for EarHover in terms of signal discrimination and user acceptability. We then evaluated the gesture detection and classification performance of two prototype devices (in-ear type/open-ear type) for real-world application scenarios.

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%.

In this study, we propose a new input method for mobile and wearable computing using facial expressions. Facial muscle movements induce physical deformation in the ear canal. Our system utilizes such characteristics and estimates facial expressions using the ear canal transfer function (ECTF). Herein, a user puts on earphones with an equipped microphone that can record an internal sound of the ear canal. The system transmits ultrasonic band-limited swept sine signals and acquires the ECTF by analyzing the response. An important novelty feature of our method is that it is easy to incorporate into a product because the speaker and the microphone are equipped with many hearables, which is technically advanced electronic in-ear-device designed for multiple purposes. We investigated the performance of our proposed method for 21 facial expressions with 11 participants. Moreover, we proposed a signal correction method that reduces positional errors caused by attaching/detaching the device. The evaluation results confirmed that the f-score was 40.2% for the uncorrected signal method and 62.5% for the corrected signal method. We also investigated the practical performance of six facial expressions and confirmed that the f-score was 74.4% for the uncorrected signal method and 90.0% for the corrected signal method. We found the ECTF can be used for recognizing facial expressions with high accuracy equivalent to other related work.