Monitoring the occurrence count of abnormal respiratory symptoms helps provide critical support for respiratory health. While this is necessary, there is still a lack of an unobtrusive and reliable way that can be effectively used in real-world settings. In this paper, we present EchoBreath, a passive and active acoustic combined sensing system for abnormal respiratory symptoms monitoring. EchoBreath novelly uses the speaker and microphone under the frame of the glasses to emit ultrasonic waves and capture both passive sounds and echo profiles, which can effectively distinguish between subject-aware behaviors and background noise. Furthermore, A lightweight neural network with the 'Null' class and open-set filtering mechanisms substantially improves real-world applicability by eliminating unrelated activity. Our experiments, involving 25 participants, demonstrate that EchoBreath can recognize 6 typical respiratory symptoms in a laboratory setting with an accuracy of 93.1%. Additionally, an in-the-semi-wild study with 10 participants further validates that EchoBreath can continuously monitor respiratory abnormalities under real-world conditions. We believe that EchoBreath can serve as an unobtrusive and reliable way to monitor abnormal respiratory symptoms.
https://dl.acm.org/doi/10.1145/3706598.3714171
The provision of audio augmented reality (AAR) experiences is becoming more widespread. In this study, to investigate the influence of device design on AAR experience from the perspective of acoustic transparency, physical and subjective evaluations were conducted using five devices with different shapes and transparency modes. In the subjective evaluation, perceived transparency, impressions of real-world sound, and subjective impressions of AAR experience when wearing each device were evaluated for two distinct content types. We found that device design can potentially influence impressions of real-world sound, such as auditory source width, listener envelopment and punch, and subjective impressions during AAR experience. Devices with high transparency were more likely to draw attention to real-world sounds when users were experiencing AAR, and the experience was evaluated as enjoyable and natural. Two demonstration experiments showed that adding virtual sounds by open-ear earphones to real contents can provide acoustic effects such as distance enhancement.
https://dl.acm.org/doi/10.1145/3706598.3713907
Wireless earbuds are an appealing platform for wearable computing on-the-go. However, their small size and out-of-view location mean they support limited different inputs. We propose finger identification input on earbuds as a novel technique to resolve these problems. This technique involves associating touches by different fingers with different responses. To enable it on earbuds, we adapted prior work on smartwatches to develop a wireless earbud featuring a magnetometer that detects fields from a magnetic ring. A first study reveals participants achieve rapid, precise earbud touches with different fingers, even while mobile (time: 0.98s, errors: 5.6%). Furthermore, touching fingers can be accurately classified (96.9%). A second study shows strong performance with a more expressive technique involving multi-finger double-taps (inter-touch time: 0.39s, errors: 2.8%) while maintaining high accuracy (94.7%). We close by exploring and evaluating the design of earbud finger identification applications and demonstrating the feasibility of our system on low-resource devices.
https://dl.acm.org/doi/10.1145/3706598.3714133
We introduce FlexEar-Tips, a dynamic ear tip system designed for the next-generation hearables. The ear tips are controlled by an air pump and solenoid valves, enabling size adjustments for comfort and functionality. FlexEar-Tips includes an air pressure sensor to monitor ear tip size, allowing it to adapt to environmental conditions and user needs. In the evaluation, we conducted a preliminary investigation of the size control accuracy and the minimum amount of variability of haptic perception in the user's ear. We then evaluated the user's ability to identify patterns in the haptic notification system, the impact on the music listening experience, the relationship between the size of the ear tips and the sound localization ability, and the impact on the reduction of humidity in the ear using a model. We proposed new interaction modalities for adaptive hearables and discussed health monitoring, immersive auditory experiences, haptics notifications, biofeedback, and sensing.
https://dl.acm.org/doi/10.1145/3706598.3714177
Improper toothbrushing practices persist as a primary cause of oral health issues such as tooth decay and gum disease. Despite the availability of high-end electric toothbrushes that offer some guidance, manual toothbrushes remain widely used due to their simplicity and convenience. We present SmarTeeth, an earable-based toothbrushing monitoring system designed to augment manual toothbrushing with functionalities typically offered only by high-end electric toothbrushes, such as brushing surface tracking. The underlying idea of SmarTeeth is to leverage in-ear microphones on earphones to capture toothbrushing sounds transmitted through the oral cavity to ear canals through facial bones and tissues. The distinct propagation paths of brushing sounds from various dental locations to each ear canal provide the foundational basis for our methods to accurately identify different brushing locations. By extracting customized features from these sounds, we can detect brushing locations using a deep-learning model. With only one registration session (~2 mins) for a new user, the average accuracy is 92.7% for detecting six regions and 75.6% for sixteen tooth surfaces. With three registration sessions (~6 mins), the performance can be boosted to 98.8% and 90.3% for six-region and sixteen-surface tracking, respectively. A key advantage of using earphones for monitoring is that they provide natural auditory feedback to alert users when they are overbrushing or underbrushing. Comprehensive evaluation validates the effectiveness of SmarTeeth under various conditions (different users, brushes, orders, noise, etc.), and the feedback from the user study (N=13) indicates that users found the system highly useful (6.0/7.0) and reported a low workload (2.5/7.0) while using it. Our findings suggest that SmarTeeth could offer a scalable and effective solution to improve oral health globally by providing manual toothbrush users with advanced brushing monitoring capabilities.
https://dl.acm.org/doi/10.1145/3706598.3713893