Cuff-less methods, especially pulse wave analysis (PWA) techniques with PPG/mmWave sensing, have shown great potential for non-intrusive blood pressure (BP) monitoring. However, the state-of-the-art solutions are only validated on small-scale healthy subjects, neglecting patients with abnormal BP and thus a more urgent need for BP monitoring. To bridge the gap, we first build the largest mmWave-BP dataset to our knowledge, including 930 real patients with cardiovascular diseases, and perform extensive experiments, which reveals that all existing PWA methods exhibit far less satisfactory performance with standard deviation errors (STD) exceeding 16 mmHg for systolic BP (SBP) and 11mmHg for diastolic BP (DBP). An in-depth investigation shows that physiological factors have complex effect on vascular elasticity and structure, thus people with very different BP values may exhibit extremely similar pulse waveform, which leads to confusion in model learning. In this work, we propose BP3, which fuses physiological factors into sensing-data-driven deep-learning framework, so as to capture the intricate effect of physiological factors during the whole process of learning pulse waveforms. Evaluation results show that BP3 achieves the mean errors of-1.57 mmHg and -0.34 mmHg, STD of 9.77 mmHg and 7.93 mmHg for SBP and DBP, respectively. Moreover importantly, BP3 shows remarkable gain particularly for subjects with abnormal BP, achieving mean errors that are only 0.48% ~ 20.86% of the state-of-the-art solutions.

Prior research has demonstrated that scene cues within real-world environments significantly expedite visual search tasks, particularly when co-occurrence objects—those that tend to appear concurrently and in close proximity—are present. The underlying mechanisms driving these enhancements remain a subject of ongoing debate. In this study, we recruited forty-five participants to perform a visual search task using real-world scene images, with conditions involving either the presence or absence of co-occurrence objects, while recording their right eye movements. Our findings revealed that the presence of co-occurrence objects exclusively enhanced search accuracy, without impacting response times or fixation durations. As participants engaged in repeated searches, their performance improved, characterized by increased accuracy, reduced response times, fewer fixations, and shorter fixation durations, applicable to both target objects and co-occurrence objects alike. Notably, the effect of co-occurrence objects remained consistent across search iterations, indicating no change with accumulated search experience. Collectively, these outcomes suggest a strategic role for co-occurrence objects in guiding visual search processes, potentially mediated through the spatial processing of target objects. This study contributes to our understanding of how contextual cues influence visual attention and search precision in complex environments.

Wireless sensing offers a promising approach to identify the content of liquids without opening the container or directly touching the liquid. Although existing methods aim to achieve fine-grained identification, i.e., distinguishing a 1% v/v difference in alcohol content, they still have limitations in detecting highly deceptive counterfeit liquors that have much smaller content differences, sometimes as low as 0.2% v/v alcohol content. In this paper, we propose mm Taster, a mobile system that combines the mmWave radar with a smartphone to perform fine-grained and robust alcohol sensing. To achieve the desired fine granularity, we introduce a novel feature extraction model that exploits theunique reflection responses across multiple mmWave frequencies, which provide discriminative information about liquid content. Furthermore, we observe the serious interference of target displacement on identification performance, which hinders the various applications in mobile scenarios. To enhance the robustness, mm Taster incorporates a customizedtranslation-invariantneural network,ConvNet, to remove the location interference and extract stable liquid-dependent features regardless of target displacement. Extensive experimental results demonstrate that mm Taster can accurately distinguish the alcohol differences as low as0.2% v/vwith an accuracy of over90.8%even in scenarios involving diverse displacements and rotations.