arXiv:2606.23534v2 Announce Type: replace Abstract: Contactless in-bed body-position inference can be limited by the sensor representation the host receives rather than by the classifier. We characterize a bedside 60 GHz frequency-modulated continuous-wave (FMCW) radar that reports only on-device constant-false-alarm-rate (CFAR) point clouds, together with a low-resolution thermal array (24x32 nominal, 24x16 effective), on 20 subjects under leave-one-subject-out (LOSO) evaluation. Two tasks share the cohort: 273 operator-directed in-bed posture holds (148.7 minutes) and a per-second bed-presence timeline (8,817 scored seconds). The cohort is a friends-and-family convenience sample (13 minors, ages 5 to 68, six families), and every session used the same bed in one room with the subject uncovered, so the figures describe this setup rather than a population. Bed presence reaches a 0.871 per-subject median balanced accuracy (95% CI 0.796 to 0.910). For four-class posture, nested LOSO, in which every selection and fitting step sees training subjects only, gives 0.646 balanced accuracy (per-subject median 0.683). Prone recall is 0.45 at a precision of 0.42, so prone is not usefully detected. Version 1 reported 0.674 for a stacked ensemble picked on the folds it was scored on; this version replaces that figure with a nested estimate and corrects the bed-presence accounting and the description of the recording setting. Thermal resolves left versus right (lateral swaps of 35 to 42% with radar alone, 8% with thermal alone). The supine-versus-prone breathing cue shows up in the CFAR output only as a group shift (Cohen's d = 0.61), with a clean per-hold peak in 8.4% of holds. The next hardware test is raw range-FFT access, not further classifier tuning on CFAR detections.