Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff based or newly emerging wearable based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this article, we explore contactless BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates the arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size commercial off-the-shelf millimeter-wave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with a mean error of –0.30 mmHg and –0.23 mmHg, as well as a standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.

Arrhythmia is a common problem of irregular heartbeats, which may lead to serious complications such as stroke and even mortality. Due to the paroxysmal nature of arrhythmia, its long-term monitoring and early detection in daily household scenarios, instead of depending on ECG examination only available during clinical visits, are of critical importance. While ambulatory ECG Holter and wearables like smartwatches have been used, they are still inconvenient and interfere with users' daily activities. In this paper, we bridge the gap by proposing mmArrhythmia, which employs low-cost mmWave radar to passively sense cardiac motions and detect arrhythmia, in an unobtrusive contact-less way. Different from previous mmWave cardiac sensing works focusing on healthy people, mmArrhythmia needs to distinguish the minute and transient abnormal cardiac activities of arrhythmia patients. To overcome the challenge, we custom-design an encoder-decoder model that can perform arrhythmia feature encoding, sampling and fusion over raw IQ sensing data directly, so as to discriminate normal heartbeat and arrhythmia. Furthermore, we enhance the robustness of mmArrhythmia by designing multichannel ensemble learning to solve the model bias problem caused by unbalanced arrhythmia data distribution. Empirical evaluation over 79,910 heartbeats demonstrates mmArrhythmia's ability of robust arrhythmia detection, with 97.32% accuracy, 98.63% specificity, and 92.30% sensitivity.

Respiratory rate is of great value in health care, especially when it can be continuously monitored using wearable devices in daily life. Recent works employ Photoplethysmography (PPG) on smartwatch for continuous respiration monitoring, based on a certain medical discovery called Respiratory Sinus Arrhythmia (RSA), which describes the relationship between respiratory and heart rate. However, we find that these works fall short of robustness. In particular, the respiratory estimation accuracy drops significantly when people breathe faster (e.g., after sports). We further identify the root reason that the RSA gradually weakens as the respiratory rate increases. In this paper, we propose BreathAnalyzer, which can estimate respiratory rate accurately even at high respiratory rates. To achieve this, BreathAnalyzer boosts the weakened RSA and also handles the motion artifacts, by integrating features from multiple domains, i.e., frequency, time and non-linear Poincare domain, instead of using the single spectrum or raw signal in previous studies. Moreover, BreathAnalyzer custom-designs a tree-based learning model, which fits multi-domain features, while considering limitations of smartwatch. We implement BreathAnalyzer prototype on COTS smartwatch, and extensive evaluation demonstrates that BreathAnalyzer outperforms the state-of-the-art approaches, with accuracy improvement by 35.37% ∼ 80.42% across a variety of practical scenarios including high respiratory rates.

Recent years have witnessed great advancement of representation learning (RL)-based models for the knowledge graph relation prediction task. However, they generally rely on structure information embedded in the encyclopedic knowledge graph, while the beneficial semantic information provided by lexical knowledge graph is ignored, leading the problem of shallow understanding and coarse-grained analysis for knowledge acquisition. Therefore, this article introduces concept information derived from the lexical knowledge graph (e.g., Probase), and proposes a novel Hierarchical Attention model for Relation Prediction, which consists of entity-level attention mechanism and concept-level attention mechanism, to throughly integrate multiple semantic signals. Experimental results demonstrate the efficiency of the proposed method on two benchmark datasets.

Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-called explicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This work introduces a new concept called implicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.37x improvement compared with two baselines.