Hengyu Yu

Effective heating, ventilation, and air conditioning (HVAC) systems are increasingly vital for public health, safeguarding indoor air quality and occupant safety across residential, occupational, and educational settings. Accurate, real-time monitoring of airflow speed is essential for optimizing HVAC performance and ensuring healthy environments. However, existing solutions face significant limitations: traditional anemometers are intrusive and incur high deployment costs, acoustic methods raise severe privacy concerns and are highly susceptible to environmental noise, and contact-based vibration sensors struggle to isolate blade micro-vibrations from overwhelming chassis noise. Millimeter-wave (mmWave) radar emerges as a compelling alternative, offering non-contact, privacy-preserving, and noise-robust sensing. Furthermore, it significantly reduces the Total Cost of Ownership (TCO) by multiplexing existing smart building sensors for a zero-marginal-cost deployment. Yet, the core challenge of reliably distinguishing and extracting fan blade micro-vibrations from dominant structural vibrations using commodity mmWave radar in noisy HVAC environments remains largely unaddressed. Motivated by this gap and the potential of mmWave sensing, we propose mmVibFlow, a mmWave radar-based method for airflow speed estimation by capturing the micro-vibrations of fan blades. mmVibFlow identifies the operating gear (speed level) of an air conditioner and predicts real-time airflow speed from the machine’s vibrations. To effectively distinguish mechanical chassis vibrations from the target fan blade micro-vibrations, we develop an interference-resistant vibration sensing framework. This framework first utilizes beamforming to decorrelate vibrations from the blade region and the chassis, then employs phase-based subtraction to isolate the fan blade vibrations. To extract informative features for airflow speed estimation from RF signals, we develop a signal representation method that captures multi-domain frequency features at different airflow speeds. We validate mmVibFlow, achieving 99% accuracy in gear classification and a mean squared error (MSE) of 0.05 in airflow speed estimation when the radar is placed within 1.5 meters of the air conditioner outlet. Experimental results demonstrate that mmVibFlow accurately measures airflow speed in real-world environments.

Early detection of myocardial infarction (MI) is essential for alleviating symptoms and improving daily activity performance. Researchers typically employ continuous segments of heartbeat signals (20–30 seconds), such as ECG signals, for MI detection, as MI often induces changes in heartbeat patterns. Current MI detection methods, like wearable sensors, may induce discomfort from prolonged wear, and Radio Frequency (RF) based approaches might fail to extract fine-grained heartbeat signals during vigorous movement. This article presents a reliable and motion-robust MI detection method based on RF signals. By developing a series of advanced signal processing algorithms, MI-Ra can capture fine-grained heartbeat signals during various daily activities. Our design is inspired by the fact that RF reflections caused by heartbeat signals are mixed with other motion-induced reflections in a nonlinear manner. We utilize the Taylor series expansion method to extract the linear component of these mixed non-linear signals and propose a novel Generative Adversarial Networks (GAN) method, named IQ-TransGAN, to separate the heartbeat signal. To enhance MI detection reliability, MI-Ra employs a multi-periodicity modeling method to extract refined signal representations from recovered heartbeat signals. We have recruited 50 volunteers with MI from Zhongnan Hospital of Wuhan, China, and 50 volunteers without MI, for comprehensive evaluations. The results demonstrate that MI-Ra achieves an average MI detection accuracy of 95.2% when user is quasi-stationary. Even during user non-stationary conditions, MI-Ra maintains an average detection accuracy of 90.5%. MI-Ra shows promise in paving way for smart home healthcare.