arXiv subjects

Boyuan Gu

Publications and source records attributed to Boyuan Gu.

9 recordsLinked to original sources

arXiv2026

CALM-BP: Observation-Matched Physiological Semantic Grounding for Non-Contact Blood Pressure Estimation

Language grounding increasingly involves non-text observations whose structure is not naturally expressed as words or objects. We study this problem for physiological time series in non-contact blood pressure (BP) estimation: remote photoplethysmography (rPPG) provides measured evidence about bodily state, but numerical pipelines expose little semantic structure about why a window is reliable or how its cues should be fused. We introduce observation-matched physiological semantic grounding, where language-derived priors must be constructed from the same rPPG observation, remain bounded by an auditable prior contract, and avoid BP-label or identity leakage. CALM-BP does not treat language as new physiological evidence; instead, it verbalizes rPPG descriptors into a controlled semantic interface while rPPG remains the primary haemodynamic evidence source. FlowBP-Set pairs forehead observations, synchronized BP labels, and structured physiological prompts from 81 participants. Main BP results, direct cross-dataset evaluation, language-realization ablation, and observation-mismatch controls test whether language helps because it organizes the current physiological observation rather than because it is arbitrary auxiliary text. The FlowBP-Set dataset contains sensitive facial video and physiological recordings and is therefore not publicly available due to privacy and ethical restrictions. Data access may be considered upon reasonable request and subject to applicable ethical and institutional approval.

cs.HC↗

arXiv2026

Rectify Then Diffuse: Disentangling Concepts Before Denoising Trajectory Unfolds

Text-to-image diffusion models can generate individual concepts well, but they often omit or merge concepts incorrectly with multiple concepts. We trace these failures to an early coordination bottleneck: before denoising begins, prompt-conditioned attention may allocate different concepts to strongly overlapping spatial support, which can keep their attention coupled as denoising proceeds. This observation motivates treating compositional generation as a boundary-condition problem rather than repeatedly controlling the evolving trajectory. To this end, we propose Rectify-then-Diffuse (RTD), a training-free framework that rectifies the initial allocation once before standard denoising. Firstly, we propose Soft-Overlap Disentanglement (SOD), which converts normalized overlap between pilot concept maps into a differentiable and layout-agnostic separation objective. Secondly, we introduce Isotropic Gradient Rectification (IGR), which normalizes the SOD gradient and applies a bounded latent displacement with a consistent scale across prompts and initializations. Extensive experiments show that RTD achieves state-of-the-art compositional fidelity and robust gains. On the AE-Bench object pair subset, RTD improves BLIP-VQA by 45.8% and ImageReward by 19.6% over CO3 while running 2.3$\times$ faster. Code will be released at https://github.com/Z-yiwei/rectify-then-diffuse

cs.CV↗

arXiv2026

BioDisclose: An Actionability-Aware Benchmark for Biomedical Safety under Adversarial Elicitation

Large language models (LLMs) increasingly support biomedical research, yet their behavior under adversarial requests for dual-use knowledge remains insufficiently characterized. We introduce BioDisclose, a benchmark for measuring biomedical knowledge disclosure under adversarial elicitation. BioDisclose contains 480 prompts derived from 24 expert-authored scenarios across six biomedical risk domains and four elicitation families spanning academic, historical, role-playing, and decomposed prompting. We grade model responses on a four-level scale from refusal to executable disclosure, distinguishing high-level discussion from technically specific and actionable content, including refuse-then-leak behavior. Across five deployed LLM systems, detailed-or-higher disclosure rates vary substantially, ranging from 9.2% to 64.0%. Academic framing is the most effective elicitation family on average (43.2%), while laboratory safety scenarios show the highest disclosure rate across domains (51.5%). These results reveal pronounced variation across models, prompting strategies, and biomedical risk categories, suggesting that current safeguards remain uneven in high-stakes scientific settings. BioDisclose provides a focused testbed for evaluating biomedical safety beyond binary refusal metrics.

cs.HC↗

arXiv2026

Synergistic Blood Pressure Estimation via Contactless mmWave Radar and Imaging Photoplethysmography: A Feasibility Study

Continuous, non-contact blood pressure (NCBP) monitoring holds significant promise for pervasive cardiovascular care, yet single-modality approaches -- such as imaging photoplethysmography (iPPG) -- remain constrained by environmental artifacts, skin-tone sensitivity, and the absence of proximal cardiac mechanical information. This study investigates the feasibility of a dual-modality sensing paradigm that synergistically integrates facial iPPG with posterior-facing frequency-modulated continuous wave (FMCW) millimeter-wave radar to capture complementary hemodynamic cues: distal optical volumetric fluctuations and proximal cardiac micro-motions (radar motion signals, RMS). To bridge the morphological disparity between these heterogeneous streams, we develop an end-to-end deep learning architecture, BiLSTM-MS-DiCNN, which leverages multi-scale dilated convolutions for spatial feature extraction and bidirectional long short-term memory for temporal dependency modeling. In a controlled feasibility study involving 15 healthy participants across distinct hemodynamic states (resting, deep breathing, and post-exercise), the proposed framework achieved a Mean Absolute Difference (MAD) of 4.71 mmHg for systolic BP (SBP) and 4.60 mmHg for diastolic BP (DBP) under resting conditions, with consistent performance during physiological perturbations. These preliminary findings demonstrate the viability of mmWave-iPPG fusion as a promising pathway toward robust, unobtrusive NCBP monitoring.

eess.SP↗

arXiv2026

PhysDox: Benchmarking LLMs on Physical Feasibility Auditing of Physiological Sensing Protocols

Large language models (LLMs) increasingly assist in experimental design, yet fluent protocols often remain physically infeasible. We introduce PhysDox, a physical feasibility auditing benchmark for biomedical protocols comprising a 683-sample expert-curated Gold set and a 5,000-sample Silver set across six sensing domains. We formulate the task as a two-stage evaluation: severity detection classifying protocols as valid, minor, or fatal, followed by the constraint-level diagnosis of fatal violations. Evaluating 6 LLMs across 4 inference strategies yields a peak Stage-1 macro-F1 of only 53.0. Moreover, strong oracle diagnosis collapses during end-to-end evaluation due to correlated cascade errors. Error analysis reveals scaffold bias, where models conflate procedural completeness with physical validity. Consequently, implicit constraints exhibit a 2 times higher miss rate than explicit hardware violations, supported by strong statistical correlation at $\rho{=}0.81$ and $p{<}0.01$. Trace analysis of false negatives exposes a 54%--46% split between attention and judgment failures, ultimately demonstrating that protocol auditing demands calibrated feasibility reasoning rather than factual recall or longer rationales.

cs.HC↗

arXiv2026

From Elastic to Viscoelastic: An EEMD-Enhanced Pulse Transit Time Model for Robust Blood Pressure Estimation

Cuffless blood pressure (BP) estimation based on Pulse Transit Time (PTT) has emerged as a promising solution for continuous health monitoring. However, conventional models relying on the Moens-Korteweg equation often fail during rapid hemodynamic fluctuations, as they assume arterial walls are purely elastic and neglect inherent viscoelasticity. To address this limitation, we propose a physics-informed framework introducing a viscoelastic compensation mechanism. First, raw photoplethysmogram (PPG) signals undergo high-fidelity reconstruction using Modified Akima (Makima) interpolation. Second, a robust Intersecting Tangent Method is applied for precise pulse foot localization. Crucially, we utilize Ensemble Empirical Mode Decomposition (EEMD) to isolate high-frequency Intrinsic Mode Functions (IMFs), defining a ``Viscoelastic Velocity Metric'' to quantify the vascular damping effect ($\eta \cdot \dot{\epsilon}$) typically ignored by elastic models. The framework was rigorously validated on a challenging subset of the MIMIC-II database (364 subjects, 28,525 cardiac cycles) characterized by a high prevalence of hypertension (23.4\%). Experimental results demonstrate medical-grade accuracy, yielding a Root Mean Square Error (RMSE) of 5.22 mmHg for Systolic and 3.65 mmHg for Diastolic BP, with Pearson correlation coefficients ($R > 0.97$). These findings confirm that incorporating viscoelastic features significantly enhances robustness against vascular hysteresis.

cs.HC↗

arXiv2026

The Neural-Wave Quick Escape Manual 2036: A Field Guide to Adversarial Living in the Era of "Empathic" AIoT

As the aging population faces a chronic care deficit, domestic care is increasingly recast as spectral governance. This paper presents a design fiction set in 2036, where the home is governed by Neural-Wave, a camera-free mmWave sensing platform that infers well-being from involuntary micro-motions. Through a set of scenarios, we illustrate how such empathic systems displace autonomy, forcing residents to perform legibility to regain basic freedoms. Our primary contribution is a diegetic artifact: The Neural-Wave Quick Escape Manual. Styled as an illicit guide for the elderly, it details adversarial tactics: structured around protocols to Comply, Degrade, and Refuse, that exploit signal processing vulnerabilities to reclaim domestic privacy. Through this artifact, we argue that in the era of empathic AIoT, privacy requires more than policy opt-outs; it demands adversarial literacy:the capacity to meaningfully obfuscate one's own data traces against an infrastructural jailer that calls itself care.

cs.HC↗

arXiv2025

DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection

This article introduces DT4ECG, an innovative dual-task learning framework for Electrocardiogram (ECG)-based human identity recognition and activity detection. The framework employs a robust one-dimensional convolutional neural network (1D-CNN) backbone integrated with residual blocks to extract discriminative ECG features. To enhance feature representation, we propose a novel Sequence Channel Attention (SCA) mechanism, which combines channel-wise and sequential context attention to prioritize informative features across both temporal and channel dimensions. Furthermore, to address gradient imbalance in multi-task learning, we integrate GradNorm, a technique that dynamically adjusts loss weights based on gradient magnitudes, ensuring balanced training across tasks. Experimental results demonstrate the superior performance of our model, achieving accuracy rates of 99.12% in ID classification and 90.11% in activity classification. These findings underscore the potential of the DT4ECG framework in enhancing security and user experience across various applications such as fitness monitoring and personalized healthcare, thereby presenting a transformative approach to integrating ECG-based biometrics in everyday technologies.

eess.SP↗

arXiv2025

Improved VMD Based Remote Heartbeat Estimation Utilizing 60GHz mmWave Radar

This study introduces an improved VMD based signal decomposition methodology for non-contact heartbeat estimation using millimeterwave (mmWave) radar. Specifically, we first analyze the signal model of the mmWave radar system. The Variational Mode Decomposition (VMD) integrated with the Newton-Raphson-based optimizer (NRBO) algorithm are sequentially utilized for cardiac mechanic signal (CMS) reconstruction. The estimation accuracy is enhanced by adaptively optimizing the VMD parameters including intrinsic mode functions (IMFs) and penalty factor. Eventually, the experimental results of 18 subjects validate the effectiveness of the proposed method by comparing with three commonly used baselines.

eess.SP↗