@article{zhong_guo_gehi_xu_li_2025, title={Wearable PPG-to-Multi-Lead ECG Conversion for Cardiac Monitoring}, volume={11}, DOI={10.1109/bsn66969.2025.11337909}, abstractNote={The electrocardiogram (ECG) has been the gold standard for heart disease evaluation due to the rich information about the electrical activity of the heart contained in it. However, existing ECG monitoring devices either lack the capability for continuous monitoring or are unable to support multi-lead ECG recordings. To address the issues, we propose an approach for generating multi-lead ECG from photoplethysmogram (PPG), which can be passively monitored by wearable devices such as smartwatches. The PPG collected from wearable devices is first passed to a trained conditional diffusion model to generate the single-lead ECG, and then through a long short-term memory (LSTM) model to construct and predict the multi-lead ECG. The final outputs can be used to monitor and detect abnormal cardiac patterns in daily life. We evaluate the performance of our proposed approach with the dataset collected from dailylife scenarios involving 32 subjects. The results show that our approach can generate multi-lead ECGs accurately. In addition, a case study is conducted using data collected from the hospital, which demonstrates the effectiveness of our approach in detecting ST elevation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>ST elevation refers to an upward deviation of the ST segment on an electrocardiogram (ECG) from the baseline, indicating a potential heart attack or other cardiac issues. It is a crucial diagnostic finding in acute myocardial infarction (heart attack) and requires prompt medical attention. It is a key indicator of myocardial ischemia in practice.}, author={Zhong, Chongxin and Guo, Zhishan and Gehi, Anil Kishin and Xu, Chenhan and Li, Huining}, year={2025}, month={Nov} }
 @article{zhang_zhong_fu_chen_jia_li_xu_2025, title={mV-IMU: mmWave-Enabled Virtual Inertia Measurement Unit for High-Fidelity Activities of Daily Living Monitoring}, volume={11}, DOI={10.1109/bsn66969.2025.11337466}, abstractNote={Monitoring human motion through inertial metrics is vital for healthcare, rehabilitation, and activity recognition. Traditional approaches rely on wearable inertial measurement units (IMUs), which, despite their accuracy, impose burdens due to their intrusive nature, limiting long-term usability. To mitigate this, recent advances explore device-free alternatives, such as pose-based inertial inference from video or mmWave sensing. However, inertial signals derived from pose tracking are prone to error amplification during differentiation. In this paper, we present mV-IMU, a novel mmWave-enabled Virtual Inertial Measurement Unit framework that bypasses pose estimation altogether to directly reconstruct body accelerations from raw mmWave signals. Our approach leverages a deep inertia reconstruction model trained on kinematics-informed features extracted from mmWave point clouds, integrated with a physicsguided optimization scheme for enhanced accuracy. Extensive evaluations show that mV-IMU achieves inertial measurement fidelity close to wearable IMUs, enabling practical, non-intrusive motion monitoring for smart healthcare and rehabilitation contexts.}, author={Zhang, Zhi and Zhong, Chongxin and Fu, Yuliang and Chen, Ying and Jia, Jinyuan and Li, Huining and Xu, Chenhan}, year={2025}, month={Nov} }