mmUbiSense: Unleashing Ubiquitous mmWave Sensing via Vibration-Resilient Integration with Everyday Appliances
Duo Zhang, Xusheng Zhang, Zhehui Yin, Hongliu Yang, Wenwei Li, Zhaoxin Chang, Taiting Lu, Yuan Ni, Xiaohang Wang, Daqing ZhangMillimeter-wave (mmWave) radar has been widely studied for ubiquitous human sensing, but its deployment in homes remains limited by the need for additional power, connectivity, and dedicated hardware. A formative user study with 236 participants suggests that integrating sensing capabilities into existing home appliances may offer a more acceptable deployment model: although many participants were reluctant to install standalone sensors, 92% expressed willingness to use appliance-integrated sensing services. Motivated by this finding, we present mmUbiSense, a system that integrates low-cost commercial radar chips into everyday appliances. This integration, however, exposes the radar to mechanical vibrations generated during appliance operation, which can substantially distort both micro-motion signals and macro-motion point clouds. Existing compensation methods typically rely on high-end radar hardware and are therefore difficult to apply under the cost constraints of appliance integration. mmUbiSense addresses the two sensing regimes separately through a physics-guided framework. For micro-motion sensing, we develop a spectral-matching estimator that models the interaction between appliance vibration and physiological motion in the complex radar signal, allowing respiration and heart rates to be recovered without relying on fine spatial resolution. For macro-motion sensing, we introduce a spatio-temporal clustering method that distinguishes vibration-induced ghost points from human motion by comparing Doppler responses with actual spatial displacement. We evaluate mmUbiSense on air conditioners, washing machines, and range hoods across vital sign monitoring, pose estimation, fall detection, and gesture recognition tasks. The results show consistent improvements over the corresponding baselines in the evaluated settings, supporting the feasibility of integrating reliable mmWave sensing into everyday household appliances using low-cost hardware.