Datasets:
The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 15 fields in line 6165, saw 142
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4195, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2533, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
for key, pa_table in ex_iterable.iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 15 fields in line 6165, saw 142Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Home HAR — WiFi CSI Human Activity Recognition (Home Environment)
Dataset Description
Home HAR is a WiFi Channel State Information (CSI) dataset for human activity recognition collected in a residential (home) environment using two ESP32-C6 microcontrollers operating as commodity 802.11n access points. It contains 7 activity classes recorded across 3 temporally separated data-collection sessions, totaling approximately 2.7 million CSI packets and ~465 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 |
|---|---|
drink |
Drinking from a cup/glass |
eat |
Eating a meal at a table |
empty |
No human present in the sensing area |
sleep |
Sleeping / lying still in bed |
smoke |
Smoking (arm/hand motions) |
watch |
Watching TV (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 | Residential home, single room |
| TX–RX Distance | ~3 meters, line-of-sight |
Data Sessions
| Session | Period | Role | Files |
|---|---|---|---|
Session 1 (data1/) |
October 2025 | Train | 7 files (1 per activity) |
Session 2 (data2/) |
October 2025 | Train | 7 files (1 per activity) |
Session 3 (data3/) |
February 2026 | Test | 14 files (2 per activity) |
The ~3.5-month temporal gap between training (Sessions 1–2) and test (Session 3) evaluates model robustness to environmental drift — changes in furniture placement, ambient RF interference, seasonal temperature variations, etc.
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
- Load CSV and parse the
datacolumn into complex I/Q arrays - Select 52 LLTF subcarriers (discard guard/null subcarriers)
- Resample to a uniform 150 Hz sample rate (original rate is irregular ~100–200 Hz)
- Feature extraction: Rolling variance with window W ∈ {20, 200, 2000} (recommended: W=200)
- 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 | 46.3% |
| XGBoost | 47.8% |
| Conv1D | 52.9% |
Note: Home HAR is the most challenging dataset due to 7 fine-grained classes and the large temporal gap between train and test sessions.
License
This dataset is released under the CC BY 4.0 license.
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