MISP: Mamba-Based Inertial Shape and Pose Estimation with Ultra-Wideband Ranging and Skeletal Priors

Sparse inertial pose estimation (SIP) recovers full-body motion from body-worn inertial measurement units (IMUs), offering an occlusion-robust and portable solution for motion capture. Yet under sparse sensor layouts(e.g., 6 IMUs), the problem remains severely under-constrained and suffers from IMU drift. Recent work adds new sensing modalities (e.g., ultra-wideband [UWB] ranging) to mitigate IMU drift. However, prior work is constrained by two main challenges: (1) lack of effective means to acquire and exploit subject-specific body constraints, degrading cross-subject generalization and leaving ranging cues scale-ambiguous; (2) how to effectively exploit heterogeneous observations in temporal modeling. To address these challenges, we propose: (1) MISP, a sparse inertial framework that fuses both UWB-measured inter-sensor distances and subject-specific bone-length priors into the inertial pipeline; to obtain the skeletal prior, MISP-Shaper independently estimates explicit bone-length constraints from a short natural-motion window; (2) Continuous Unified Fusion (CUF), which repeatedly re-injects observation features across temporal blocks to preserve inertial and geometric cues; and Together, these components enable MISP to reduce joint position error by up to 12% over prior work.