gadgadgad/OfficeHAR

Office HAR — WiFi CSI Human Activity Recognition (Office Environment)

Dataset Description

Office HAR is a WiFi Channel State Information (CSI) dataset for human activity recognition collected in an office environment using two ESP32-C6 microcontrollers operating as commodity 802.11n access points. It contains 4 activity classes with approximately 0.8 million CSI packets and ~66 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

Activity Classes

Label	Description
eat	Eating a meal at a desk
empty	No human present in the sensing area
watch	Watching a screen (seated, minimal motion)
work	Working at a desk (typing, mouse use)

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	Office room with desks, chairs, and typical office furniture
TX–RX Distance	~3 meters, line-of-sight
Recorded	October 2025

Data Organization

File	Label	Approx. Packets
eat_1.csv	eat	~192K
empty_1.csv	empty	~192K
watch_1.csv	watch	~192K
work.csv	work	~192K

Split strategy: Percentage-based temporal split. The first 80% of each recording is used for training and the remaining 20% for testing. This preserves temporal ordering — the model never sees future data during training.

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	93.3%
XGBoost	91.4%
Conv1D	94.2%
CNN-LSTM	93.3%
PCA + KNN	85.6%

Office HAR demonstrates strong performance across all classifiers. The 4-class problem in an office setting is well-suited for practical deployment in workplace occupancy analytics and smart building management.

License

This dataset is released under the CC BY 4.0 license.