Continuous blood pressure (BP) monitoring using wearable devices has received increasing attention due to its importance in diagnosing diseases. However, existing methods mainly measure BP intermittently, involve some form of user effort, and suffer from insufficient accuracy due to sensor properties. In order to overcome these limitations, we study the BP measurement technology based on heart sounds, and find that the time interval between the first and second heart sounds (TIFS) of bone-conducted heart sounds collected in the binaural canal is closely related to BP. Motivated by this, we propose HearBP, a novel BP monitoring system that utilizes inear microphones to collect bone-conducted heart sounds in the binaural canal. We first design a noise removing method based on U-net autoencoder-decoder to separate clean heart sounds from background noises. Then, we design a feature extraction method based on shannon energy and energy-entropy ratio to further mine the time domain and frequency domain features of heart sounds. In addition, combined with the principal component analysis algorithm, we achieve feature dimension reduction to extract the main features related to BP. Finally, we propose a network model based on dendritic neural regression to construct a mapping between the extracted features and BP. Extensive experiments with 41 participants show the average estimation error of 0.97mmHg and 1.61mmHg and the standard deviation error of 3.13mmHg and 3.56mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range specified by the FDA’s AAMI protocol.

Off-screen handwriting recognitionenriches the handwriting interaction paradigm for mobile devices. However, the existing approaches are only applicable to the specific environment and equipment conditions. In this paper, we proposeAcouWrite, a general, scalable and real-time handwriting recognition system based on active acoustic sensing. In detail, AcouWrite relies onactive acoustic sensingusing only a pair of microphones and speakers on the smartphone to capture real-time handwriting input. Particularly, we extract theshort-time dCIR (st-dCIR)to monitor the changes in the acoustic transmission channel resulting from finger movement. Technically, we use aCNN-GRUclassifier to complete the recognition task in AcouWrite. Moreover, we use data augmentation and spelling error correction methods to improve AcouWrite's robustness. To improve the generalization of our AcouWrite for new characters, we incorporate the transfer learning module into our AcouWrite. In various real-world environments, experiments demonstrate that AcouWrite achieves a mean recognition accuracy of 97.62%, a word accuracy (WA) of 96.4% and a character error rate (CER) of 1.5% for 100 common words, and an average response time of 94 milliseconds.

Bowel sound (BS) is an important physiological signal of the human body, which is also an objective reflection of gastrointestinal motility. However, BS has characteristics of weak signal, strong noise, and randomicity, which bring great challenges to the daily detection of BS. In this paper, we propose BSMonitor, the first BS monitoring system with strong noise-resistant capability via earphones. BSMonitor uses one earphone attached to the abdomen to collect BS signals and the other earphone worn in the ear to collect external noises and internal noises. After eliminating the noises through the Kalman filter and band-pass filter, the signal containing BS is separated via the empirical mode decomposition. Then BSMonitor extracts MFCC features of BS signals and applies a carefully-designed LSTM network to perform highly-accurate BS detection. Finally, an alert mechanism calculates the frequency and duration of detected BS and compares with the normal values to alert users. Furthermore, to increase the amount and diversity of training data, we introduce a data augmentation method, which can further improve the accuracy and generalization of BSMonitor. Through extensive experiments with 18 volunteers, we find that BSMonitor not only achieves high accuracy of BS detection but also has strong generalization across different users and environments. Particularly, BSMonitor achieves accuracy up to 98.73% and 94.56% in thebenchmark experimentsand thecross experiments, respectively.

With the development of society and the gradual increase of life pressure, the number of people engaged in mental work and working hours have increased significantly, resulting in more and more people in a state of fatigue. It not only reduces people’s work efficiency, but also causes health and safety related problems. The existing fatigue detection systems either have different shortcomings in diverse scenarios or are limited by proprietary equipment, which is difficult to be applied in real life. Motivated by this, we propose a multi-information fatigue detection system named WakeUp based on commercial smart speakers, which is the first to fuse physiological and behavioral information for fine-grained fatigue detection in a non-contact manner. We carefully design a method to simultaneously extract users’ physiological and behavioral information based on the MobileViT network and VMD decomposition algorithm respectively. Then, we design a multi-information fusion method based on the statistical features of these two kinds of information. In addition, we adopt an SVM classifier to achieve fine-grained fatigue level. Extensive experiments with 20 volunteers show that WakeUp can detect fatigue with an accuracy of 97.28%. Meanwhile, WakeUp can maintain stability and robustness under different experimental settings.

With the rapid development of mobile devices and the fast increase of sensitive data, secure and convenient mobile authentication technologies are desired. Except for traditional passwords, many mobile devices have biometric-based authentication methods (e.g., fingerprint, voiceprint, and face recognition), but they are vulnerable to spoofing attacks. To solve this problem, we study new biometric features which are based on the dental occlusion and find that the bone-conducted sound of dental occlusion collected in binaural canals contains unique features of individual bones and teeth. Motivated by this, we propose a novel authentication system, TeethPass, which uses earbuds to collect occlusal sounds in binaural canals to achieve authentication. We design an event detection method based on spectrum variance and double thresholds to detect bone-conducted sounds. Then, we analyze the time-frequency domain of the sounds to filter out motion noises and extract unique features of users from three aspects: bone structure, occlusal location, and occlusal sound. Finally, we design an incremental learning-based Siamese network to construct the classifier. Through extensive experiments including 22 participants, the performance of TeethPass in different environments is verified. TeethPass achieves an accuracy of 96.8% and resists nearly 99% of spoofing attacks.