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 (44 in the laboratory, 17 more in a living room and an industrial site), plus 7 participants of a physiological reference subset, performed 21 gestures, 10 activities, and 6 sentiment expressions, recorded at the same time by 13 mmWave radars (8 on the ground, 5 on the ceiling), passive RFID tags on the arms, a LoRa link, body-worn IMUs, and 24 infrared motion-capture cameras.

Paper: RF-Behavior: A Multimodal Radio-Frequency Dataset for Activity and Affective Behavior Analysis — Si Zuo, Yuqing Song, Jin Han, Sahar Golipoor, Ying Liu, Xujun Ma, Petter Holme, Stephan Sigg — arXiv:2511.06020. Project website: https://sizuo.github.io/RF-Behavior/

The dataset has three environments: the laboratory (environment 1, all five modalities), a living room (2) and an industrial site (3), the latter two with the radars and the IMUs. The physiological reference subset adds simultaneous ECG and EEG recordings in the living room.

1. Contents

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

Environment	Modalities	Participants	Trials (C1 / C2 / C3)
1 laboratory	radar, LoRa, RFID, motion capture, IMU (C3); C4 RFID only	44	17,030 (4,193 / 2,084 / 133 / 10,620)
2 living room	radar, IMU; ECG and EEG in the reference subset	24	2,206 (1,492 / 641 / 73)
3 industrial site	radar, IMU	9	1,182 (1,009 / 160 / 13)

The same participant number is the same person in every environment: U01 to U44 took part in the laboratory (29 of them in C1 to C3, 17 in C4), U45 to U61 in the living room and the industrial site, U62 to U68 in the physiological reference subset (living room).

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, thus its time stamps come from the RFID reader clock (12 min 35 s ahead of the radar clock).

Physiological reference subset

Seven new participants (U62 to U68) completed the campaign 3 affective protocol in the living room while ECG, EEG, IMU, and radar were recorded at the same time. ECG was recorded with a Shimmer3 ExG unit at 512 Hz with four disposable electrodes (RA and LA below the right and left clavicle, RL and LL on the right and left lower abdomen); the files hold two status columns and the four calibrated channels LA-RA, LL-LA, LL-RA and Vx-RL in mV. EEG was recorded with a NeuroSky MindWave Mobile headset: one channel at 512 Hz, the active electrode on the left frontal forehead, reference and ground on the left ear; raw samples in ADC units, plus the device's 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 (0-100) at about 1 Hz. The streams were started and stopped at approximately coordinated times; the time stamps of the recording computers align them, and the reference time of a phase is the radar start.

The subset holds the 34 phases in which all four modalities were recorded. Eight phases were discarded because one modality is missing: the ECG of U64 (E03, E04), U66 (E05, E06) and U67 (E05, E06) had no usable time information, the radar of U64 E06 was not recorded, and the IMU of U67 E01 was not recorded (meta/excluded_physio_sessions.csv). Only 9 of the 13 radars ran in this subset (radars 4, 6, 8 and 12 are absent in most phases), and some IMU files are empty; the trial table says so for every phase.

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), next to the radar and IMU zips of the same phases. The rows of meta/trials_LivingRoom.csv with subset = physiological_reference carry the ECG and EEG facts; meta/self_assessment_physio.csv holds the per-phase self-ratings (0 to 5) of the seven participants. The physiological recordings are a per-subject reference for the induced conditions, not a population-level sample.

Modality	Sensor	Rate	Content of one trial
`radar`	13 × TI IWR1443 mmWave radars, 77–81 GHz	about 30 frames/s (ground), 5–7 frames/s (ceiling)	Point clouds: time, x, y, z, signal strength, for each radar
`lora`	Semtech SX1276 LoRa node + USRP receiver, 865.5 MHz	200 Hz features	Amplitude of the baseband signal, its difference, and its variance (20 Hz)
`rfid`	6 Alien AZ 9662 tags on the arms (C4: 9), Impinj Speedway R420 reader	about 50 reads/s in total	One row per tag read: time stamp, tag EPC, RSSI, phase
`mocap`	24 infrared cameras (motion capture)	100 Hz	Position and rotation of rigid bodies: chest, arms; in C2 and C3 also hips and legs
`imu`	3 Movesense sensors (chest, right arm, left arm; in C2 chest, right arm, right leg)	104 Hz	Acceleration, angular rate, magnetic field; in the laboratory C3 only
`ecg`	Shimmer C9EB	500 Hz	Two status values and four calibrated differential channels
`eeg`	one raw EEG channel	nominal 512 Hz raw; 1 Hz summary	Raw samples, signal quality, attention, meditation, and band features

In total: 42,758 zips, 1.7 GB; the laboratory alone has 16 million radar points, 3.9 million RFID reads and about 11 hours of radar recordings.

Segments, with one segment per trial and modality, and one segment per radar file (each of the 13 radars):

Campaign (laboratory)	Total segments
C1 gestures	66,167
C2 activities	31,943
C3 sentiment expressions	2,258
C4 gestures at antenna distances	10,620
Total laboratory	110,988
Living room	26,947
Industrial site	13,880
Total	151,815

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, thus 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: the 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, 1.5 m; 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, 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	Position of the sensor: `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 IMUs. 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` sentiment expressions; names in `meta/classes.csv`. The trial table says which campaign a trial belongs to.
repetition	Repetition number of the class by this participant
time	Start of the trial, `YYYYMMDDhhmmss`, local time (Europe/Helsinki). The same value in all zips of one trial.

The available zips of one trial share the same name apart from the node and the modality.

Radar files

radar_<n>.npz holds points, an array with one row per detected point (time in Unix seconds, x, y, z in metres in the frame of the radar, 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 (Section 3) and in scripts/Radar/read_vis.py (to_global; views points and animation), run_ground.py, and run_ceiling.py.

Trial table

meta/trials_<Environment>.csv has one row per trial with: participant, class, class name, repetition, the reference time, the height of the ceiling radars (5 m or 3 m), and for each modality whether it exists, its own start time, its duration, and extra facts (missing radars, number of radar points, RFID reads, rigid bodies, IMU sensors, empty IMU files). Physiological rows also include the self-rating, ECG and EEG statistics, missing internal streams, synchronization basis, and quality notes. meta/self_assessment_physio.csv stores only anonymous participant identifiers.

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. The calls are the same as in the OctoNet loader:

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 reference subset, request environment: [2], users 62 to 68, and modalities radar, imu, ecg, and eeg. The ECG reader returns status and channel arrays. The EEG reader returns raw and summary dictionaries.

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

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 in the style of the OctoNet demo: download with the OctoNet demo's streaming.py and its config.json (the copy in scripts/demo/ differs in three lines, marked), load_recording, iter_segments, and show_keyframe for every modality (scripts/demo/rfb_demo.py).

Nothing here depends on OctoNet code; only the file-name convention, the loader calls, and the demo calls follow it, 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:

@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.

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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