Tracking the Turn: Mamba-Powered Human Orientation Detection using UWB

User orientation is crucial for many context-aware applications, including interactive museum experiences, smart door access, and intuitive human-environment interaction. However, most existing indoor localization systems focus on estimating position, while body orientation is typically assigned to secondary devices such as inertial measurement units. In this paper, we propose a purely UWB-based approach that predicts yaw orientation directly from UWB Channel Impulse Response (CIR) measurements recorded at fixed anchors as they receive transmissions from a single wearable tag. We use a bidirectional Mamba architecture that captures dependencies across the anchor observations through forward and backward recurrent scans. The model uses per-anchor CIR and a body-part conditioning module to adapt the representation to different tag placements on the body. Two different Kalman filters are used as post-processing stages to exploit temporal continuity: an orientation-based filter that smooths the neural network predictions, and a location-based filter that additionally incorporates position-derived heading corrections. We evaluated the model's performance in different scenarios to ensure generalizability. The proposed Mamba model achieves a mean absolute error of 38.6 degrees in its raw form, outperforming a rule-based baseline of 49.5 degrees. With the location-based Kalman filter, the error is further reduced to 18.9 degrees, corresponding to a 51% reduction.