Human Walking Sensing and Pose Estimation in the 6 GHz Band Using Amplitude and Phase CSI

This paper investigates human pose estimation from Orthogonal Frequency-Division Multiplexing (OFDM) signals in an indoor multistatic wireless network operating in the 6 GHz band. We design and validate a processing pipeline that exploits both the amplitude and phase of the Channel State Information (CSI) from multiple radio links to estimate the human body pose. Four deep learning architectures from the literature, namely DT-Pose, MetaFi++, HPE-Li, and VST-Pose, are adapted to the OFDM CSI structure and extended to jointly exploit the amplitude and phase information. The models estimate the pose of a human walking within the network coverage area. Performance evaluation is conducted on an open-access dataset using standard pose-estimation metrics such as Procrustes-aligned Mean Per-Joint Position Error (PA-MPJPE) and Bone Length Loss (BLL). Results indicate that reliable human pose reconstruction can be achieved from 6 GHz OFDM CSI measurements, with DT-Pose providing the best overall accuracy. On average, amplitude-only CSI yields performance comparable to joint amplitude-phase processing, whereas phase information is more beneficial as a complementary feature rather than as a standalone input.