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Songqin Cheng

Publications and source records attributed to Songqin Cheng.

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PPG as a Bridge: Cross-Device Authentication for Smart Wearables with Photoplethysmography

As smart wearable devices become increasingly powerful and pervasive, protecting user privacy on these devices has emerged as a critical challenge. While existing authentication mechanisms are available for interaction-rich devices such as smartwatches, enabling on-device authentication (ODA) on interaction-limited wearables including rings, earphones, glasses, and wristbands remains difficult. Moreover, as users increasingly own multiple smart devices, relying on device-specific authentication methods becomes redundant and burdensome. To address these challenges, we present PPGTransID, a ubiquitous and unobtrusive cross-device authentication (CDA) approach that leverages the real-time physiological consistency of photoplethysmography (PPG) signals across the human body. PPGTransID utilizes widely available PPG sensors on wearable devices to capture users' physiological signals and compares them with remote PPG (rPPG) signals extracted from a smartphone camera, where robust face-based authentication is already established. In doing so, PPGTransID securely transfers the reliable authentication status of the smartphone to nearby wearable devices without requiring additional user interaction. An evaluation with 33 participants shows that PPGTransID achieves a balanced accuracy of 95.5 percent and generalizes across multiple wearable form factors. Robustness experiments with 10 participants demonstrate resilience to variations in lighting, camera placement, and user behavior, while a real-time usability study with 14 participants confirms reliable performance with minimal interaction burden.

cs.HC

Non-Contact Health Monitoring During Daily Personal Care Routines

Remote photoplethysmography (rPPG) enables non-contact, continuous monitoring of physiological signals and offers a practical alternative to traditional health sensing methods. Although rPPG is promising for daily health monitoring, its application in long-term personal care scenarios, such as mirror-facing routines in high-altitude environments, remains challenging due to ambient lighting variations, frequent occlusions from hand movements, and dynamic facial postures. To address these challenges, we present LADH (Long-term Altitude Daily Health), the first long-term rPPG dataset containing 240 synchronized RGB and infrared (IR) facial videos from 21 participants across five common personal care scenarios, along with ground-truth PPG, respiration, and blood oxygen signals. Our experiments demonstrate that combining RGB and IR video inputs improves the accuracy and robustness of non-contact physiological monitoring, achieving a mean absolute error (MAE) of 4.99 BPM in heart rate estimation. Furthermore, we find that multi-task learning enhances performance across multiple physiological indicators simultaneously. Dataset and code are open at https://github.com/McJackTang/FusionVitals.

cs.CV