RF-Behavior is released for academic research only, under CC BY-NC-SA 4.0. By requesting access you confirm that you will use the data for non-commercial research, that you will not try to identify the participants, and that you will cite the paper in work that uses the data.

RF-Behavior

A multimodal radio-frequency dataset for activity and affective behavior analysis: 61 participants, 21 gestures, 10 activities, and 6 affective behaviors, recorded at the same time by 13 mmWave radars, RFID tags, a LoRa link, body-worn IMUs, and 24 motion-capture cameras, in a laboratory, a living room, and an industrial site. A reference subset for affective behavior adds ECG and EEG.

Environment	Modalities	Participants	Trials
1 laboratory	radar, LoRa, RFID, motion capture, IMU (C3)	44	17,030
2 living room	radar, IMU; ECG and EEG for 7 participants	24	2,206
3 industrial site	radar, IMU	9	1,182

42,928 files, 1.9 GB. One zip per trial and modality; meta/ holds the trial tables. Paper: arXiv:2511.06020. Website: https://sizuo.github.io/RF-Behavior/

Get the data: accept the terms above, log in once (hf auth login), then

python scripts/download/download_rfbehavior.py --campaign C1 --user 1 3 4 --modality radar lora --out ./RF-Behavior

--campaign: C1 gestures, C2 activities, C3 affective behaviors, C4 gestures at RFID antenna distances. --user: participant numbers 1 to 68; not every participant is in every campaign or environment, see meta/participants.csv and the trial tables. --modality: radar, lora, rfid, mocap, imu, ecg, eeg; not every modality exists in every environment, see the table below. --out: the target folder. Without a filter the script downloads everything.

1. What is in the data

Campaign	Classes	Participants (laboratory)	Trials (laboratory)	Typical length
C1 gestures	21 hand and arm gestures	38 (25)	6,694 (4,193)	3.5 s
C2 activities	10 activities: walking, running, sitting, lying, stairs, ball sports	33 (26)	2,885 (2,084)	6 s
C3 affective behaviors	focus, distraction, stress, relaxation, depression, excitement	38 (23)	219 (133)	2.5 min
C4 gestures at antenna distances	the 21 gestures of C1, RFID only, antenna at 1.5 m or 3 m, in front or at the side	17 (17)	10,620	1.5 s

Modality	Sensor	Rate	Content of one trial
`radar`	13 × TI IWR1443, 77–81 GHz (8 on the ground, 5 on the ceiling)	30 frames/s ground, 5–7 ceiling	Point clouds: time, x, y, z, signal strength, per radar
`lora`	Semtech SX1276 node + USRP receiver, 865.5 MHz	200 Hz features	Baseband amplitude, its difference, its variance (20 Hz)
`rfid`	6 Alien AZ 9662 tags on the arms (C4: 9), Impinj R420 reader	about 50 reads/s	One row per read: time, tag, RSSI, phase
`mocap`	24 infrared cameras	100 Hz	Position and rotation of rigid bodies
`imu`	3 Movesense sensors on the body	104 Hz	Acceleration, angular rate, magnetic field
`ecg`	Shimmer3 ExG, 4 electrodes	512 Hz	Four calibrated channels in mV, two status values
`eeg`	NeuroSky MindWave Mobile, left forehead	512 Hz raw, 1 Hz summary	Raw samples; signal quality, 8 band powers, attention, meditation

Participants. The same number is the same person in every environment: U01 to U44 laboratory (29 in C1 to C3, 17 in C4), U45 to U61 living room and industrial site, U62 to U68 the ECG/EEG subset. Gender, age range, body measures (shoulder width, head-to-shoulder distance, arm length), and the campaigns of every participant: meta/participants.csv; the age and gender table is in the statistics block below. The self-ratings after each C3 state (0 to 5): meta/self_assessment.csv.

Not yet uploaded: the laboratory IMU recordings of C1 and C2, because of a technical issue during the data collection. They follow once the issue is solved; laboratory IMU covers C3 for now.

C4: the RFID antenna positions

In C4 the participant repeated the gestures of C1 while the RFID antenna stood at one of four positions. The position is the node of the zip and its folder: 13 = 1.5 m in front (as in C1 to C3, folder rfid/), 15 = 1.5 m at the side (rfid_15/), 16 = 3 m in front (rfid_16/), 17 = 3 m at the side (rfid_17/); see meta/nodes.csv. C4 adds the tags A1, A5 and A9; their places on the body were not recorded. C4 has no radar, so its time stamps come from the RFID reader clock (12 min 35 s ahead of the radar clock).

Physiological reference subset (ECG and EEG)

Seven participants (U62 to U68) did the C3 protocol in the living room with ECG, EEG, IMU and radar recorded together. ECG: Shimmer3 ExG, 512 Hz, four electrodes (RA and LA below the clavicles, RL and LL on the lower abdomen); channels LA-RA, LL-LA, LL-RA, Vx-RL in mV. EEG: NeuroSky MindWave Mobile, one channel at 512 Hz, electrode on the left forehead, reference and ground on the left ear; raw samples in ADC units plus, at about 1 Hz, the signal-quality index, eight band powers (delta, theta, low and high alpha, low and high beta, low and mid gamma) and the eSense attention and meditation scores (0 to 100). The streams start and stop together; the recording computers' time stamps align them, and the reference time of a phase is the radar start.

The subset holds the 34 phases with all four modalities. Eight phases were left out because one modality is missing (meta/excluded_physio_sessions.csv). Only 9 of the 13 radars ran in this subset. Files: ecg/x_ecg_2_<user>_<E..>_1_<time>.zip (ecg.csv) and eeg/x_eeg_2_<user>_<E..>_1_<time>.zip (raw_eeg_512hz.csv, summary_1hz.csv). The rows of meta/trials_LivingRoom.csv with subset = physiological_reference carry the ECG and EEG facts; the column ecg_eeg_states of meta/self_assessment.csv lists the recorded states. The recordings are a per-subject reference for the induced states, not a population sample.

Statistics: participants by age and gender, segments per campaign

Age range	Women	Men	All	Laboratory	Living room and industrial site
under 18	1	2	3	3	0
18–20	2	1	3	2	1
21–30	19	25	44	35	9
31–40	5	0	5	2	3
41–50	2	2	4	2	2
not recorded	2	0	2	0	2
All	31	30	61	44	17

Gender and age of U62 to U68 were not recorded.

Segments (one per trial and modality, one per radar file):

Campaign	Segments
Laboratory C1 / C2 / C3 / C4	66,167 / 31,943 / 2,258 / 10,620
Living room	26,947
Industrial site	13,880
Total	151,815

The laboratory alone has 16 million radar points, 3.9 million RFID reads and about 11 hours of radar recordings.

2. Files and names

One zip for each trial and modality, in flat folders (a folder on the Hub holds at most 10,000 files, so the C4 antenna positions 15, 16, 17 have their own folders):

radar/   r_radar_1_1_M10_1_20250710163420.zip    radar_00.npz ... radar_12.npz
lora/    5_lora_1_1_M10_1_20250710163420.zip     abs_200Hz.csv, diff_200Hz.csv, var_20Hz.csv
rfid/    13_rfid_1_1_M10_1_20250710163420.zip    rfid.csv   (node 13: antenna in front at 1.5 m; C1 to C4)
rfid_15/ 15_rfid_1_2_M01_1_20240726143130.zip    rfid.csv   (C4: antenna at the side; rfid_16/ 3 m front, rfid_17/ 3 m side)
mocap/   14_mocap_1_1_M10_1_20250710163420.zip   mocap.csv
imu/     x_imu_1_3_E01_1_20250720180733.zip      Chest_acc_data.csv, Chest_gyro_data.csv, ...
ecg/     x_ecg_2_62_E01_1_20261002152433.zip      ecg.csv
eeg/     x_eeg_2_62_E01_1_20261002152433.zip      raw_eeg_512hz.csv, summary_1hz.csv
meta/    trials_Lab.csv, trials_LivingRoom.csv, trials_Industry.csv, participants.csv, self_assessment.csv, classes.csv, nodes.csv, packing_log_<Env>.csv
scripts/ readers, loader, download and unpack scripts, demo notebook

The file name is <node>_<modality>_<environment>_<user>_<class>_<repetition>_<time>.zip:

Field	Meaning
node	`r` all radars (the radar number is in the file name inside the zip), `5` LoRa (next to radar 5), `13` RFID antenna, `14` infrared cameras, `x` body-worn sensors. Positions in `meta/nodes.csv`.
environment	`1` laboratory, `2` living room, `3` industrial site
user	Participant number
class	`M01`–`M21` gestures (C1 and C4), `A01`–`A10` activities, `E01`–`E06` affective behaviors; names in `meta/classes.csv`
repetition	Repetition of the class by this participant
time	Start of the trial, `YYYYMMDDhhmmss`, local time (Europe/Helsinki); the same in all zips of one trial

Radar files and the trial table

radar_<n>.npz holds points, one row per detected point (time in Unix seconds, x, y, z in metres in the radar's frame, signal strength), and frame, the frame number of each row. The radars remove static reflections. The transformation to the global frame (origin at the standing point, z up, x towards radar 5) is in the paper and in scripts/Radar/read_vis.py (to_global), run_ground.py, and run_ceiling.py.

meta/trials_<Environment>.csv has one row per trial: participant, class, repetition, reference time, height of the ceiling radars (5 m or 3 m), and for each modality whether it exists, its start time, its duration, and extra facts (missing radars, radar points, RFID reads, rigid bodies, IMU sensors, empty IMU files). Physiological rows add the self-rating, ECG and EEG statistics, and quality notes. All meta files hold anonymous participant numbers only.

3. Download

The dataset is gated: accept the terms on this page, then log in once (hf auth login). Download a selection with the script in scripts/download/:

python download_rfbehavior.py --campaign C1 --user 1 3 4 --modality radar lora --out ./RF-Behavior
python download_rfbehavior.py --config selection.json --out ./RF-Behavior
python download_rfbehavior.py --campaign C3 --user 62 63 --modality radar imu ecg eeg --out ./RF-Behavior

or with the Hub library:

from huggingface_hub import snapshot_download
snapshot_download("Si-Z/RF-Behavior", repo_type="dataset", local_dir="RF-Behavior",
                  allow_patterns=["meta/*", "radar/r_radar_1_1_M*", "lora/5_lora_1_1_M*"])

4. Load

scripts/loader/rfbehavior_loader.py reads the downloaded folder:

from rfbehavior_loader import get_dataset, get_dataloader

config = {"environment": [1], "campaign": ["C1"], "user_list": None, "class_list": None,
          "node_id": [5, 6, 7], "modality": ["radar", "lora", "rfid", "mocap"], "require_all": True}
dataset = get_dataset(config, "RF-Behavior")
sample = dataset[0]
sample["label"], sample["class_name"]            # 9, 'arms swing'
sample["modality_data"]["radar"][5]["points"]    # (N, 5) array
loader = get_dataloader(dataset, batch_size=4, shuffle=True)

For the ECG/EEG subset request environment: [2], users 62 to 68, and the modalities radar, imu, ecg, eeg.

scripts/<Modality>/read_vis.py draws one trial: radar point clouds in the room (3-D animation), LoRa features, RFID tag motion on a body map, motion-capture skeletons, IMU signals. They need numpy, pandas, and matplotlib and work on the recording layout:

python scripts/download/unpack_release.py --download ./RF-Behavior --out ./RF-Behavior_unpacked
python scripts/Radar/read_vis.py --root ./RF-Behavior_unpacked/Lab/Radar --view animation --campaign C2 --user U01 --cls A01

scripts/demo/demo.ipynb is a notebook demo: download a selection with streaming.py and a config.json, then load_recording, iter_segments, and show_keyframe for every modality (scripts/demo/rfb_demo.py).

Note. The file names, the loader calls (get_dataset, get_dataloader), and the demo calls follow the convention of the OctoNet dataset, so that the two datasets can be used side by side.

5. License and citation

The data: CC BY-NC-SA 4.0, academic research only. The scripts in scripts/: MIT license (see scripts/LICENSE).

Please cite the paper: Si Zuo, Yuqing Song, Jin Han, Sahar Golipoor, Ying Liu, Xujun Ma, Petter Holme, and Stephan Sigg. RF-Behavior: A Multimodal Radio-Frequency Dataset for Activity and Affective Behavior Analysis. arXiv:2511.06020. A revised version of the paper is under final review at IMWUT; the entry is updated on publication.

@article{zuo2025rfbehavior,
  title   = {RF-Behavior: A Multimodal Radio-Frequency Dataset for Activity and Affective Behavior Analysis},
  author  = {Zuo, Si and Song, Yuqing and Han, Jin and Golipoor, Sahar and Liu, Ying and Ma, Xujun and Holme, Petter and Sigg, Stephan},
  journal = {arXiv preprint arXiv:2511.06020},
  year    = {2025}
}

Contact: Si Zuo, Aalto University, si.zuo@aalto.fi

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Paper for Si-Z/RF-Behavior

RF-Behavior: A Multimodal Radio-Frequency Dataset for Human Behavior and Emotion Analysis

Paper • 2511.06020 • Published Nov 8, 2025