gadgadgad/HomeOccupancy

Home Occupation — WiFi CSI Occupancy Detection (Home Environment)

Dataset Description

Home Occupation is a WiFi Channel State Information (CSI) dataset for room-level occupancy detection collected in a residential (home) environment using two ESP32-C6 microcontrollers operating as commodity 802.11n access points. It contains 3 occupancy classes recorded across 3 temporal sessions per class, totaling approximately 1.7 million CSI packets and ~150 minutes of continuous recording.

This dataset is part of the research paper:

WiFi Sensing-Based Human Activity Recognition For Smart Home Applications Using Commodity Access Points Gad Gad, Iqra Batool, Mostafa M. Fouda, Shikhar Verma, Zubair Md Fadlullah IEEE, 2026

📄 Paper · ⚡ GitHub · 🌐 Project Page

Occupancy Classes

Label	Description
empty	No human present in the sensing area
sleep	Person sleeping / lying still in bed (minimal motion)
work	Person working at a desk (typing, mouse use, subtle motion)

This is a coarse-grained occupancy task — the goal is to detect whether the room is empty, or if someone is present and performing a low-motion or moderate-motion activity. This is directly relevant to smart-home energy management, HVAC automation, and security systems.

Collection Setup

Parameter	Value
Hardware	2 × ESP32-C6 (TX: AP mode, RX: STA mode)
WiFi Standard	802.11n, 20 MHz bandwidth, HT-LTF
Subcarriers	64 total (52 LLTF data subcarriers extracted)
Packet Rate	~200 packets/sec (irregular, resampled to 150 Hz)
Transport	UART serial @ 115200 baud
Environment	Residential home, single room
TX–RX Distance	~3 meters, line-of-sight

Data Organization

File	Label	Split	Approx. Packets
empty_1.csv	empty	Train	~193K
empty_2.csv	empty	Train	~193K
empty_3.csv	empty	Test	~193K
sleep_1.csv	sleep	Train	~193K
sleep_2.csv	sleep	Train	~193K
sleep_3.csv	sleep	Test	~193K
work_1.csv	work	Train	~193K
work_2.csv	work	Train	~193K
work_3.csv	work	Test	~193K

Split strategy: File-based temporal holdout. The last recording session per label is used as the test set, ensuring the model is evaluated on temporally distinct data.

CSV Format

Each CSV file contains one row per received CSI packet with the following columns:

Column	Description
type	Packet type (always CSI_DATA)
seq	Sequence number / local timestamp
mac	Transmitter MAC address
rssi	Received Signal Strength Indicator (dBm)
rate	PHY rate index
noise_floor	Noise floor estimate (dBm)
fft_gain	FFT gain applied by hardware
agc_gain	Automatic Gain Control value
channel	WiFi channel number
local_timestamp	ESP32 local timestamp (µs)
sig_len	Signal length
rx_state	Receiver state
len	CSI data length (128 = 64 subcarriers × 2 components)
first_word	Header word
data	Raw CSI data as [I₀, Q₀, I₁, Q₁, ..., I₆₃, Q₆₃] — 128 signed integers representing in-phase and quadrature components for 64 subcarriers

Recommended Preprocessing Pipeline

1. Load CSV and parse the data column into complex I/Q arrays
2. Select 52 LLTF subcarriers (discard guard/null subcarriers)
3. Resample to a uniform 150 Hz sample rate (original rate is irregular ~100–200 Hz)
4. Feature extraction: Rolling variance with window W ∈ {20, 200, 2000} (recommended: W=200)
5. Windowing: Segment into fixed-length windows (e.g., 100 samples = 0.67s at 150 Hz)

Benchmark Results

Best results from the paper using rolling-variance features (W=200):

Classifier	Accuracy
Random Forest	98.7%
Conv1D	100.0%
PCA + KNN	69.9%

Home Occupation achieves near-perfect classification, demonstrating that WiFi CSI can reliably distinguish between empty rooms, sleeping, and active desk work — even with commodity hardware.

License

This dataset is released under the CC BY 4.0 license.