CSI-GaitCov: A Wi-Fi channel state information dataset for gait-based person identification across walking-direction...

This data article presents CSI-GaitCov, a structured Wi-Fi Channel State Information (CSI) dataset developed to support gait-based person identification under controlled walking-direction and backpack-carrying covariates. The dataset was collected from 25 anonymized male participants aged 25-40 years in an indoor laboratory environment using a TP-Link Archer C6 V2.80 (AC1200) access point and an Intel AX210 Wi-Fi 6 network interface card with the PicoScenes CSI acquisition platform. Each participant completed 100 walking trials of approximately 6 s, yielding a total of 2,500 recordings. The protocol included 80 normal-walking and 20 backpack-carrying trials per participant, with walking direction balanced between left-to-right and right-to-left trajectories; the backpack condition involved an approximate load of 1 kg. CSI was acquired using a 2 × 2 MIMO configuration with an 80 MHz channel bandwidth and 245 retained CSI tones, providing four CSI channel sequences for each valid packet. The original packet-level recordings are released as CSV files containing timestamps, received signal strength indicator (RSSI), acquisition-configuration metadata, and serialized interleaved in-phase and quadrature CSI values, while a corresponding processed NPZ representation is provided for every trial. During preprocessing, valid complex CSI coefficients were reconstructed, transformed to log-amplitude, and temporally standardized onto a uniform 512-sample grid, producing fixed-size CSI arrays of 4 × 512 × 245 with aligned time and RSSI sequences. The release further includes a complete trial manifest, data dictionary, predefined benchmark splits, a 46-dimensional four-stream descriptive feature table, technical-validation summaries, benchmark results, and a reproducible processing and evaluation notebook. All 2500 raw trials were successfully processed, with a mean rejected-packet proportion of 0.0675%. CSI-GaitCov can support research on Wi-Fi sensing, gait biometrics, person identification, CSI preprocessing and representation learning, feature extraction, covariate-aware evaluation, and the development of robust wireless human-sensing methods.