Energy‐Efficient Edge Computing for Real‐Time Skeleton Pose Reconstruction in Sustainable Remote Health Monitoring

ABSTRACT Traditional imaging‐ and audio‐based sensing systems often face issues with environmental interference, privacy, and security. Wi‐Fi Channel State Information offers a non‐invasive alternative for human behaviour sensing but lacks precision in full‐body activity recognition. This study presents a sustainable edge computing system that integrates a 3D Convolutional Neural Network and a Bidirectional Gated Recurrent Unit (Bi‐GRU) with attention for real‐time human skeleton pose reconstruction. By aligning Wi‐Fi CSI with Kinect‐captured posture data, the system extracts spatial–temporal features to generate accurate 3D skeletal models. It accurately identifies trunk and posture by combining the precision of vision‐based recognition with the non‐invasive advantages of CSI‐based sensing. Leveraging edge computing enhances energy efficiency and reduces cloud transmission needs, making it suitable for sustainable healthcare, smart homes, and remote monitoring in resource‐limited settings.