JessicaSanson/wird_gest_wifi_gesture_monostatic_intel

WIRD-GEST Dataset

Gesture recognition dataset collected via monostatic full-duplex Wi-Fi sensing on commercial off-the-shelf (COTS) laptops — no external sensors, no dedicated transmitter, no hardware modification of any kind.

Accompanying paper: "WIRD-GEST: Gesture Recognition in the Real World Using Active Range-Doppler Wi-Fi Sensing on COTS Hardware" (Sanson et al., 2025).

---

Key Innovation: Monostatic Sensing

Most Wi-Fi sensing datasets use a bistatic setup: a separate transmitter (e.g., a router) and a receiver capture CSI between two devices. This requires coordinating two pieces of hardware and a line-of-sight path between them.

This dataset uses a monostatic setup instead. A single unmodified laptop simultaneously transmits and receives by sharing its Local Oscillator and baseband processing — the device's own self-interference becomes the sensing signal. CSI is read directly from the built-in NIC. No second device, no external transmitter, no hardware modification of any kind is required.

---

Hardware & Capture Parameters

Parameter	Value
Hardware	Lenovo ThinkPad (Wi-Fi 6E)
Bandwidth	160 MHz
Frame rate	~40 Hz
Channel	79 (Fc ≈ 6.3 GHz)
Subcarriers	512 (data subcarriers only, pilots removed)
LTF frames	2 (csi1, csi2) — 1 RX antenna

---

Dataset Overview

Property	Value
Participants	5 (users 1–5)
Gesture classes	5
Session folders	55 (50 lab + 5 café)
Total raw frames	191,442
Complete gesture instances	722
Collection environments	Lab (primary) + café/public space (cross-location subset)
Disk size	~12 GB

Sessions are pre-split into train (25 folders, 5 users × 5 gestures) and val (25 lab + 5 café = 30 folders) sets. The café subset (user 1 only) provides an out-of-environment evaluation split for cross-location generalisation.

---

Gesture Classes

Class key	Description
hand_forward_back	Forward / backward wave
hand_up_down	Up / down wave
hand_pulse	Pulse (push forward and back)
hand_clock	Clockwise circular motion
hand_side	Side-to-side wave

---

Folder Structure

gesture_wifi_monostatic_dataset/
├── dataset_metadata.json              # aggregate statistics for the full dataset
├── lenovo_user1_clock_train/
│   ├── csi.csv
│   ├── metadata.yaml
│   └── range_doppler_data_32/
│       └── range_doppler_frames_1.pkl
├── lenovo_user1_clock_val/
│   └── ...
├── lenovo_user{1-5}_{clock,front,pulse,side,up}_{train,val}/
│   └── ...                        # one folder per user × gesture × split (50 total)
└── cafe/
    └── lenovo_user1_public_space_{clock,front,pulse,side,up}_val/
        └── ...                    # cross-location evaluation subset (5 folders)

Naming convention: lenovo_user<ID>_<gesture>_<split>

Each session folder contains:

• csi.csv — calibrated CSI measurements with per-frame gesture labels
• metadata.yaml — session-level metadata (participant, gesture, date, frame statistics)
• range_doppler_data_32/range_doppler_frames_1.pkl — pre-processed range-Doppler frames

---

Data Files

csi.csv — Raw CSI

Each row is one measurement frame (~25 ms interval at 40 Hz).

Calibration State

The CSI samples are frequency-domain measurements that have already been pre-processed:

• Pilot subcarriers removed — only the 512 data subcarriers are retained.
• Phase and delay calibrated — carrier frequency offset and timing offset compensation has been applied.
• Two LTF frames averaged per measurement frame — csi1 and csi2 columns hold the two averaged LTF measurements.

The data is ready for direct 2D DFT processing to produce range-Doppler maps. No additional calibration or pilot removal is required.

CSV Columns

Column	Description
event_timeStamp	Device event timestamp (integer, ms)
unix_timestamp	Unix time in seconds (float)
channel	Wi-Fi channel number
bandwidth_MHz	Capture bandwidth in MHz
measurement_time_repetition_ms	Target frame interval in ms
frequency_carrier_MHz	Carrier frequency in MHz
subcarrier_number	Number of data subcarriers (512)
csi1-{i}-real	LTF 1, subcarrier i, real part (i = 0..511)
csi1-{i}-imag	LTF 1, subcarrier i, imaginary part (i = 0..511)
csi2-{i}-real	LTF 2, subcarrier i, real part (i = 0..511)
csi2-{i}-imag	LTF 2, subcarrier i, imaginary part (i = 0..511)
label	Gesture label: front, up, pulse, clock, or side
start_end	Gesture boundary marker: start, end, or none

start_end semantics: Each gesture instance is bracketed by a start marker at the first frame of the motion and an end marker at the last frame. Frames outside any gesture instance are marked none. This allows precise extraction of complete gesture instances from the continuous recording.

---

range_doppler_data_32/range_doppler_frames_1.pkl — Pre-processed Radar Frames

A Python pickle file containing a list of frame dictionaries, one entry per measurement frame (~40 Hz), in temporal order.

Per-frame dictionary keys

Key	Type	Description
range_doppler_snr	2D numpy array	Range-Doppler heatmap, SNR values in dB
timestamp	int	Unix timestamp — matches unix_timestamp in csi.csv

Processing pipeline applied

1. The two LTF measurements (csi1, csi2) from csi.csv are averaged per frame.
2. A 2D DFT is applied across the subcarrier (range) and time (Doppler) axes.
3. Range and Doppler axes are interpolated to the cell sizes listed below.
4. SNR is computed in dB and clipped to [5, 40] dB, then normalised to [0, 1].
5. The heatmap is resized to 64 × 64 and stored as range_doppler_snr.

Range-Doppler map properties

Property	Value
Image size	64 × 64 (range bins × Doppler bins)
Range axis	0 to 0.63 m
Velocity axis	±0.45 m/s
Range cell size	0.93 cm
Doppler cell size	0.015 m/s
Range resolution (physical)	0.93 cm (from 160 MHz bandwidth)
Doppler resolution (physical)	0.03 m/s (from 40 Hz frame rate)
Unambiguous velocity	±0.4 m/s

---

metadata.yaml — Session Metadata

A small YAML file present in every session folder summarising that session's recording.

Field	Description
dataset.user	Participant identifier (e.g. user1)
dataset.gesture	Gesture type for this session (e.g. clock)
dataset.data_function	Split: training or validation
dataset.PC	Recording machine identifier
dataset.date	Recording date (DD_MM_YYYY)
csi.number_of_gestures	Number of complete gesture instances in the session
csi.frames_min	Minimum frame count across gesture instances
csi.frames_median	Median frame count across gesture instances
csi.frames_max	Maximum frame count across gesture instances
summary.number_of_gestures	Total gesture instances (same as csi.number_of_gestures)

---

dataset_metadata.json — Aggregate Statistics

A top-level JSON file with aggregate counts across all sessions.

Field	Description
dataset_summary.total_samples	Total raw frames across all sessions (191,442)
dataset_summary.gesture_samples	Frames labelled as a gesture (77,538)
dataset_summary.none_samples	Frames labelled as background / none (113,904)
dataset_summary.train_samples	Total frames in train sessions (125,898)
dataset_summary.val_samples	Total frames in val sessions (65,544)
complete_gesture_breakdown	Complete gesture instance counts per class
folder_details	Per-session sample and gesture counts

Complete gesture instances per class:

Gesture	Instances
clock	142
front	146
pulse	145
side	145
up	144
Total	722

---

Citation

@article{sanson2025wirdgest,
  title   = {WIRD-GEST: Gesture Recognition in the Real World Using Active
             Range-Doppler Wi-Fi Sensing on COTS Hardware},
  author  = {Sanson, Jessica Barthold and Shah, Rahul C. and Zhu, Yazhou
             and Rosales, Rafael and Frascolla, Valerio},
  year    = {2026},
  note    = {IEEE ICC 2026 Workshop},
}

---