WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch

While just-in-time interventions (JITIs) have effectively targeted common health behaviors, individuals often have unique needs to intervene in personal undesirable actions that can negatively affect physical, mental, and social well-being. We present WatchGuardian, a smartwatch-based JITI system that empowers users to define custom interventions for these personal actions with a small number of samples. For the model to detect new actions based on limited new data samples, we developed a few-shot learning pipeline that finetuned a pre-trained inertial measurement unit (IMU) model on public hand-gesture datasets. We then designed a data augmentation and synthesis process to train additional classification layers for customization. Our offline evaluation with 26 participants showed that with three, five, and ten examples, our approach achieved an average accuracy of 76.8%, 84.7%, and 87.7%, and an F1 score of 74.8%, 84.2%, and 87.2% We then conducted a four-hour intervention study to compare WatchGuardian against a rule-based intervention. Our results demonstrated that our system led to a significant reduction by 64.0 +- 22.6% in undesirable actions, substantially outperforming the baseline by 29.0%. Our findings underscore the effectiveness of a customizable, AI-driven JITI system for individuals in need of behavioral intervention in personal undesirable actions. We envision that our work can inspire broader applications of user-defined personalized intervention with advanced AI solutions.

A Plug-and-Play Temporal Normalization Module for Robust Remote Photoplethysmography

Remote photoplethysmography (rPPG) extracts PPG signals from subtle color changes in facial videos, showing strong potential for health applications. However, most rPPG methods rely on intensity differences between consecutive frames, missing long-term signal variations affected by motion or lighting artifacts, which reduces accuracy. This paper introduces Temporal Normalization (TN), a flexible plug-and-play module compatible with any end-to-end rPPG network architecture. By capturing long-term temporally normalized features following detrending, TN effectively mitigates motion and lighting artifacts, significantly boosting the rPPG prediction performance. When integrated into four state-of-the-art rPPG methods, TN delivered performance improvements ranging from 34.3% to 94.2% in heart rate measurement tasks across four widely-used datasets. Notably, TN showed even greater performance gains in smaller models. We further discuss and provide insights into the mechanisms behind TN's effectiveness.

Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

Joint Discrete Antenna Positioning and Beamforming Optimization in Movable Antenna Enabled Full-Duplex ISAC Networks

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO2 predictions by 7.6\% and 10.6\% compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2.5\% for SpO2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO2 estimation by 17.8\%.

Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends

Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E 2, represent a significant step towards achieving Artificial General Intelligence (AGI). LM agents exhibit key characteristics of autonomy, embodiment, and connectivity, allowing them to operate across physical, virtual, and mixed-reality environments while interacting seamlessly with humans, other agents, and their surroundings. This paper provides a comprehensive survey of the state-of-the-art in LM agents, focusing on the architecture, cooperation paradigms, security, privacy, and future prospects. Specifically, we first explore the foundational principles of LM agents, including general architecture, key components, enabling technologies, and modern applications. Then, we discuss practical collaboration paradigms from data, computation, and knowledge perspectives towards connected intelligence of LM agents. Furthermore, we systematically analyze the security vulnerabilities and privacy breaches associated with LM agents, particularly in multi-agent settings. We also explore their underlying mechanisms and review existing and potential countermeasures. Finally, we outline future research directions for building robust and secure LM agent ecosystems.

Advancing Architectural Floorplan Design with Geometry-enhanced Graph Diffusion

Automating architectural floorplan design is vital for housing and interior design, offering a faster, cost-effective alternative to manual sketches by architects. However, existing methods, including rule-based and learning-based approaches, face challenges in design complexity and constrained generation with extensive post-processing, and tend to obvious geometric inconsistencies such as misalignment, overlap, and gaps. In this work, we propose a novel generative framework for vector floorplan design via structural graph generation, called GSDiff, focusing on wall junction generation and wall segment prediction to capture both geometric and semantic aspects of structural graphs. To improve the geometric rationality of generated structural graphs, we propose two innovative geometry enhancement methods. In wall junction generation, we propose a novel alignment loss function to improve geometric consistency. In wall segment prediction, we propose a random self-supervision method to enhance the model's perception of the overall geometric structure, thereby promoting the generation of reasonable geometric structures. Employing the diffusion model and the Transformer model, as well as the geometry enhancement strategies, our framework can generate wall junctions, wall segments and room polygons with structural and semantic information, resulting in structural graphs that accurately represent floorplans. Extensive experiments show that the proposed method surpasses existing techniques, enabling free generation and constrained generation, marking a shift towards structure generation in architectural design.

AUGlasses: Continuous Action Unit based Facial Reconstruction with Low-power IMUs on Smart Glasses

Recent advancements in augmented reality (AR) have enabled the use of various sensors on smart glasses for applications like facial reconstruction, which is vital to improve AR experiences for virtual social activities. However, the size and power constraints of smart glasses demand a miniature and low-power sensing solution. AUGlasses achieves unobtrusive low-power facial reconstruction by placing inertial measurement units (IMU) against the temporal area on the face to capture the skin deformations, which are caused by facial muscle movements. These IMU signals, along with historical data on facial action units (AUs), are processed by a transformer-based deep learning model to estimate AU intensities in real-time, which are then used for facial reconstruction. Our results show that AUGlasses accurately predicts the strength (0-5 scale) of 14 key AUs with a cross-user mean absolute error (MAE) of 0.187 (STD = 0.025) and achieves facial reconstruction with a cross-user MAE of 1.93 mm (STD = 0.353). We also integrated various preprocessing and training techniques to ensure robust performance for continuous sensing. Micro-benchmark tests indicate that our system consistently performs accurate continuous facial reconstruction with a fine-tuned cross-user model, achieving an AU MAE of 0.35.

G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios

Modern information querying systems are progressively incorporating multimodal inputs like vision and audio. However, the integration of gaze -- a modality deeply linked to user intent and increasingly accessible via gaze-tracking wearables -- remains underexplored. This paper introduces a novel gaze-facilitated information querying paradigm, named G-VOILA, which synergizes users' gaze, visual field, and voice-based natural language queries to facilitate a more intuitive querying process. In a user-enactment study involving 21 participants in 3 daily scenarios (p = 21, scene = 3), we revealed the ambiguity in users' query language and a gaze-voice coordination pattern in users' natural query behaviors with G-VOILA. Based on the quantitative and qualitative findings, we developed a design framework for the G-VOILA paradigm, which effectively integrates the gaze data with the in-situ querying context. Then we implemented a G-VOILA proof-of-concept using cutting-edge deep learning techniques. A follow-up user study (p = 16, scene = 2) demonstrates its effectiveness by achieving both higher objective score and subjective score, compared to a baseline without gaze data. We further conducted interviews and provided insights for future gaze-facilitated information querying systems.

Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones

Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras. Despite advancements, existing datasets fall short in terms of size and diversity, limiting comprehensive evaluation under diverse conditions. This paper presents an in-depth analysis of the VitalVideo dataset, the largest real-world rPPG dataset to date, encompassing 893 subjects and 6 Fitzpatrick skin tones. Our experimentation with six unsupervised methods and three supervised models demonstrates that datasets comprising a few hundred subjects(i.e., 300 for UBFC-rPPG, 500 for PURE, and 700 for MMPD-Simple) are sufficient for effective rPPG model training. Our findings highlight the importance of diversity and consistency in skin tones for precise performance evaluation across different datasets.