Intelligent Nursing Bed: Prospective Applications of Multimodal Monitoring and Edge AI

To address the challenges of caregiving for the bedridden elderly, traditional nursing beds often suffer from limited interaction and a lack of real‐time health monitoring. Most existing smart solutions rely heavily on cloud computing, which introduces significant system latencies (> 200 ms) and poses severe privacy risks for sensitive health data. This paper presents a fully localized intelligent nursing bed system designed for low‐latency and high‐privacy closed‐loop care. The hardware architecture is centered on the RK3588 embedded platform, integrated with a 6 TOPS NPU, and fuses a 24 GHz frequency‐modulated continuous‐wave (FMCW) millimeter‐wave radar with vision‐based sensing. At the circuit level, a specialized dual‐layer control board was developed to provide physical isolation between logic and execution units. This design utilizes 74HC238D decoding logic to expand control commands and incorporates a hardware‐level limit protection circuit based on 74HC08D AND gates, ensuring deterministic response and mechanical safety. To overcome feature extraction challenges and background interference, our framework enhances YOLOv8n with four key modules: C2f‐DysnakeConv for hand feature capture, SPPF‐LSKA for multiscale fusion, Hierarchical Attention Module (HAM) for background suppression, and Detect_DBB for efficient detection. For physiological monitoring, we employ EMD and Hilbert transform to extract vital signs from radar phase data. On the RK3588 platform, the optimized model achieves 94.85% precision and 93.76% mailto:mAP@0.5 at 53.1 FPS on the NBG‐7 gesture dataset. The system maintains end‐to‐end latency under 50 ms and vital sign errors within ±2 bpm (heart rate) and ±1.5 breaths/min (respiration), establishing a robust paradigm for privacy‐preserving, real‐time intelligent care through Edge AI and specialized hardware integration.