LiBre: Toward Motion-Resilient Contactless Respiration Monitoring Using Mobile LiDAR

In this paper, we present LiBre, a LiDAR-based system for real-time respiration monitoring that remains accurate and continuous even under device motion. LiBre addresses the core challenge of disentangling large-scale device movement from subtle thoracoabdominal motions by integrating three key components: (i) an Object-Centric Feature Extraction module that produces clean, geometrically consistent human point clouds and enables multi-target sensing with minimal environmental interference; (ii) a Contrastive Registration framework that combines standard Iterative Closest Point (ICP) and masked Iterative Closest Point (MICP) to decouple device motion from respiration-induced displacements; and (iii) a Directional Residual Projection strategy that automatically estimates the RoI and projects residual motion along the dominant respiratory axis, eliminating the need for manual annotation. Following a brief autonomous stationary initialization phase to establish the respiratory RoI and optimized weights, we implement a complete prototype and validate its real-time performance. Experiments with 10 participants demonstrate that LiBre achieves respiration monitoring with < 1 BPM error at sensing distances up to 4 m, under device motion speeds up to 30 cm/s, and at orientation angles up to 60°, while supporting multi-person scenarios. The system processes each frame within 120 ms, meeting the requirements for real-time mobile health monitoring in practical applications.