Current touch-based interactions on earphones are limited by hygiene concerns and the small interaction surface. Recent works attempt to bypass these issues with mid-air gesture systems using active acoustic sensing. However, these signals may be audible and pose potential hearing risks. To address this, we propose FingerBar, a mid-air gesture recognition system for earphones that relies solely on microphones without active signal transmission. FingerBar leverages the distinctive friction sounds generated by finger gestures to achieve gesture recognition. We design a gesture filtering pipeline to maintain robustness against daily noise. An adversarial training strategy further enhances user-independent performance. From a set of 16 gestures, we identify the 7 most suitable for FingerBar based on user acceptability. Extensive evaluations demonstrate high accuracy and robustness. Furthermore, a user study confirms the practicality and acceptability of the system. Our findings highlight the promise of passive acoustic sensing as a user-friendly interaction modality for earphones.

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution, however, these systems have various applicability issues such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present MobileAF , a novel smartphone-based AF detection system using speakers and microphones. In order to capture minute cardiac activities, we propose a multi-channel pulse wave probing method. In addition, we enhance the signal quality by introducing a three-stage pulse wave purification pipeline. What’s more, a ResNet-based network model is built to implement accurate and reliable AF detection. We collect data from 23 participants utilizing our data collection application on the smartphone. Extensive experimental results demonstrate the superior performance of our system, with 98.4% accuracy, 97.6% precision, 95.8% recall, 99.2% specificity, and 96.7% F1 score.

We propose FlexibleBP, a novel cuffless blood pressure monitoring system using a wrist-worn flexible sensor to enhance comfort and accuracy. By capturing pulse wave signals from the radial artery, we develop a personalized estimation framework incorporating a Transformer model with fine-tuning. Experiments with 36 participants confirm FlexibleBP's accuracy, meeting AAMI standards. This work marks a step toward more user-friendly, advanced wearable BP monitoring solutions.

This paper addresses a significant problem in American Sign Language (ASL) translation systems that has been overlooked. Current designs collect excessive sensing data for each word and treat every sentence as new, requiring the collection of sensing data from scratch. This approach is time-consuming, taking hours to half a day to complete the data collection process for each user. As a result, it creates an unnecessary burden on end-users and hinders the widespread adoption of ASL systems. In this study, we identify the root cause of this issue and proposeGASLA–a wearable sensor-based solution that automatically generates sentence-level sensing data from word-level data. An acceleration approach is further proposed to optimize the data generation speed. Moreover, due to the gap between the generated sentence data and directly collected sentence data, a template strategy is proposed to make the generated sentences more similar to the collected sentence. The generated data can be used to train ASL systems effectively while reducing overhead costs significantly.GASLAoffers several benefits over current approaches: it reduces initial setup time and future new-sentence addition overhead; it requires only two samples per sentence compared to around ten samples in current systems; and it improves overall performance significantly.

Mobile phones nowadays are equipped with at least dual microphones. We find when a user is typing on a phone, the sounds generated from the vibration caused by finger’s tapping on the screen surface can be captured by both microphones, and these recorded sounds alone are informative enough to localize the user’s keystrokes. This ability can be leveraged to enable useful application designs, while it also raises a crucial privacy risk that the private information typed by users on mobile phones has a great potential to be leaked through such a recognition ability. In this paper, we address two key design issues and demonstrate, more importantly alarm people, that this risk is possible, which could be related to many of us when we use our mobile phones. We implement our proposed techniques in a prototype system and conduct extensive experiments. The evaluation results indicate promising successful rates for more than 4000 keystrokes from different users on various types of mobile phones.

This paper studies an important yet overlooked applicability issue in existing American sign language (ASL) translation systems. With excessive sensing data collected for each ASL word already, current designs treat every to-be-recognized sentence as new and collect their sensing data from scratch, while the amounts of sentences and the data samples per sentence are large usually. It takes a long time to complete the data collection for each single user, e.g., hours to a half day, which brings non-trivial burden to the end users inevitably and prevents the broader adoption of the ASL systems in practice. In this paper, we figure out the reason causing this issue. We present GASLA atop the wearable sensors to instrument our design. With GASLA, the sentence-level sensing data can be generated from the word-level data automatically, which can be then applied to train ASL systems. Moreover, GASLA has a clear interface to be integrated to existing ASL systems for overhead reduction directly. With this ability, sign language translation could become highly lightweight in both initial setup and future new-sentence addition. Compared with around 10 per-sentence data samples in current systems, GASLA requires 2–3 samples to achieve a similar performance.

Cyber-security issues on adversarial attacks are actively studied in the field of computer vision with the camera as the main sensor source to obtain the input image or video data. However, in modern cyber-physical systems (CPSs), many other types of sensors are becoming popularly used, such as surveillance sensors, microphones, and textual interfaces. A series of recent works investigates the adversarial attacks and the potential defenses in these noncamera sensor-based CPSs. Therefore, this article provides a systematic discussion on these existing works and serves as a complimentary summary of the adversarial attacks and defenses for CPSs beyond the field of computer vision. We first introduce a general working flow for adversarial attacks on CPSs. On this basis, a clear taxonomy is provided to organize existing attacks effectively and indicate where the defenses can be potentially performed in CPSs as well. Then, we discuss these existing attacks and defenses with detailed comparison studies. Finally, we point out concrete research opportunities to be further explored along this research direction.