IoT UWB-Radar Array System With Two-Stage Multitask Deep Network for Bed Occupancy and Posture Surveillance via Spatial Echo Feature Map and Cross-Modal Visual Explainability

Accurate, unobtrusive in-bed occupancy-posture Internet of Thing (IoT) monitoring is critical both for timely detection of 'missing patient' events, reducing false alarms in nocturnal surveillance, and informing sleep posture-related health risk management. This article presents a novel two-stage, multitask, multiple ultrawideband (UWB) radar IoT system for joint in-bed occupancy detection and fine-grained sleep posture classification, using an array of eight UWB radars. The technical novelty centers on three mechanisms: a view-weighted multiradar network (VWM-Net) that adaptively fuses single-radar echo maps into a spatial echo feature map (SEFM); an integration of DenseNet and ConvNeXt2 (DCNX2); and a cross-modal generative model that translates SEFMs into human-interpretable depth posture images. Specifically, a UCYC-GAN is proposed by integrating a U-Net structure with a CycleGAN generator, and its visual utility is compared against NICE-GAN and bidirectional diffusion bridge model (BDBM). The system is trained and validated on a cohort of 100 participants across ten fine-grained postures and four blanket conditions, supplemented with a life-sized dummy and cluttered objects to model nonhuman occupancy and environmental distractors. In the first stage, a dedicated classifier distinguishes humans from nonhuman entities with 99.09% accuracy. In the second stage, the multihead DCNX2 backbone performed multitask classification, achieving 93.75% accuracy for ten fine-grained postures, 98% for four coarse-grained postures, 88.25% for blanket-coverage types, 98.13% for blanket presence, and 95.13% for participant gender. The overall can effectively mitigate false positives from environmental clutter, advances noncontact in-bed occupancy-posture surveillance, and, through a cloud-enabled web dashboard and APIs, supports real-time visualization and retrospective data analysis.