Recent years have seen rapid advancements in ultra-wideband (UWB)-based localization systems. However, most existing solutions offer only centimeter-level accuracy and support a limited number of UWB tags, which fails to meet the growing demands of emerging sensing applications (e.g., virtual reality). This paper presents MULoc, the first system that can localize an unlimited number of UWB tags with millimeter-level accuracy. At the core of MULoc is the innovative use of UWB phase, which can provide finer-grained distance measurement than traditional time-of-f1ight (ToF) estimates. To accurately obtain phase estimates from unsynchronized devices, we introduce a novel localization scheme called anchor overhearing (AO) and eliminate raw signal errors through a signal-difference-based technique. For precise tag localization, we resolve phase ambiguity by combining a fusion-based filtering method and frequency hopping. We implement MULoc on commercial UWB modules. Extensive experiments demonstrate that our system achieves a median localization error of 0.47 mm and 90-th percentile error of 1.02 cm, reducing the error of traditional method by 91.12%.

Accurate 3D human mesh recovery from point clouds remains challenging. Most existing methods depend on full 3D supervision or complete input data, both of which are difficult to obtain in practice. %cr update This calls for robust solutions capable of handling partial point clouds in a self-supervised manner. However, the incompleteness of point clouds and the absence of supervision signals pose dual challenges. To tackle these challenges, This calls for robust solutions to handle partial point clouds in a self-supervised manner. To tackle the dual challenges of point cloud incompleteness and the absence of supervision signals, we propose a novel method named SS-HMR, which offers three key insights. First, we estimate point-wise semantics in a self-supervised manner to match partial inputs with a canonical template. The resulting correspondences serve as supervision signals for the regression network in human mesh recovery. Second, we incorporate regression-based and optimization-based paradigms into a self-improving loop: the regression network provides strong initialization for optimization, while the optimization routine generates pseudo-labels that, in turn, enhance the regression network. This mutual feedback enables more accurate and stable mesh recovery over time. Third, generating multiple initializations and selecting the best result mitigates the optimization routine's sensitivity to initialization, improving robustness to sparse and noisy data. %cr update Third, to mitigate sensitivity to initialization in the optimization routine, we generate diverse initialization candidates and transform the challenge of escaping local optima into a controllable selection task, improving robustness against sparse and noisy data. Extensive experiments are conducted on three public datasets and results demonstrate that SS-HMR outperforms existing methods. Notably, SS-HMR performs excellently on different test data, whether from original point clouds captured by depth cameras or LiDAR devices, or from noise-added ones. This shows that SS-HMR has strong generalization ability and robustness across different data sources. Codes are available at https://github.com/suchang-99/SS-HMR.

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this article, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome.

Existing contactless solutions on sleep respiration monitoring are either performed in controlled environments, having poor usability in practical scenarios, or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events of patients. In this paper, we propose Respnea, a non-invasive sleep respiration monitoring system using an impulse-radio ultra-wideband (IR-UWB) radar. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different states of subjects. Further, we construct a deep learning model which adopts a multi-headed self-attention and learn the patterns implicit in the respiration signal so as to distinguish sleep respiration events at a granularity of seconds. We conduct experiments on data collected from patients with sleep disorders and healthy subjects. The experimental results show that Respnea achieves a low error (less than 0.27 bpm) in respiration rate estimation and reaches the accuracy of 88.89% diagnosing the severity of Sleep Apnea-Hypopnea Syndrome.

Currently, cardiac diseases have become one of the biggest health concerns. Existing heartbeat monitoring methods either require dedicated intrusive devices (e.g., ECG devices) that suffer high costs or leverage video camera analyses that are light-sensitive. In this paper, leveraging the acoustic signals sent by a speaker and received by a microphone array, we develop a prototype system to achieve the contactless and low-cost heartbeat monitoring. In particular, while we exploit the passive beamforming to enhance the user's heartbeat signal, we design a filtering method in frequency domain to remove the line-of-sight (LoS) impact and retain the target-reflected signals, and propose a wideband time-delay method to estimate the direction of arrival of target-reflected signal. Thus, our prototype is able to robustly estimate the human heartbeat and push the limit of acoustic sensing range. The experimental results show that our prototype achieves a heart rate monitoring at 1.7 m with the estimation error of 0.5 bpm, which is comparable to ECG or other contact-based solutions.