Sensor Modality Versus Pretraining for Recognising Bed-Exit–Relevant Postural Transitions: A Gyroscope-Focused Feasibility Study on Public Wearable-IMU Data

Wearable ward monitoring must eventually detect when a patient leaves the bed, yet the brief postural transitions that begin a bed exit are under-examined relative to dynamic activities. As a feasibility study, we used a public waist-worn six-axis inertial dataset (30 healthy subjects) to ask which sensor axes carry these transitions and whether a pretrained foundation model helps. We defined a ten-class task of four basic activities and six directional transitions, treating the two lie-to-upright transitions as the bed-exit-relevant classes, evaluated with subject-grouped cross-validation. Postural transitions were the weakest classes, and the weakest of these was a bed-exit transition. Removing the gyroscope reduced macro-F1 from 0.822 to 0.647 and bed-exit F1 from 0.657 to 0.351 (p < 10−4) while leaving lying and walking unaffected. The mirror ablation separated the roles: the accelerometer captured posture and the gyroscope captured every directional transition. A six-axis hand-crafted model outperformed one accelerometer-only foundation model even after full fine-tuning (0.822 vs. 0.774) with far fewer labels. Scoring windows that cross bout boundaries lowered bed-exit sensitivity from 0.937 to 0.833. Among the configurations tested, sensor choice, the gyroscope in particular, mattered more than model scale. These results concern transition recognition in scripted recordings, not bed-exit detection on a ward.