---
title: 'Radio-PPG: Non-Contact PPG Sensing Dataset'
url: https://www.emergentmind.com/topics/radio-ppg-dataset
type: topic
---

# Radio-PPG: Non-Contact PPG Sensing Dataset

Searching arXiv for the cited Radio-PPG and related non-contact physiological sensing datasets.
The Radio-PPG dataset denotes a line of non-contact physiological sensing datasets in which a software-defined radio (SDR) illuminates the chest with an orthogonal frequency division multiplexing (OFDM) signal and a nearby SDR records the reflected, chest movement-modulated radio signal, while a reference photoplethysmography (PPG) sensor provides synchronized ground truth. In the original formulation, this paradigm was introduced as a method for non-contact acquisition of the PPG signal from radio reflections, producing a custom dataset of 160 minutes from 16 healthy young subjects and a waveform translation pipeline from radio segments to synthetic PPG [2402.14565]. A later paper extended the same general concept to a larger dataset of 300 minutes from 30 healthy subjects, adding three body-vital labels and evaluating deep neural representations for digital twin PPG synthesis under a 6G/WiFi integrated sensing and communication setting [2509.22326].

## 1. Conceptual definition and scope

Radio-PPG is distinct from camera-based remote photoplethysmography datasets because its primary observation channel is radio rather than video. The original study states that a software-defined radio exposes the chest of a subject sitting nearby to an OFDM signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected off the chest; the resulting dataset contains both raw radio data and the reference PPG signal [2402.14565]. The later study describes an analogous acquisition setting at 5.23 GHz using a wideband 6G/WiFi signal and frames the output as photoplethysmogram digital twin synthesis from near synchronous radio and PPG data, with additional labels for heart rate, SpO\(_2\), and respiratory rate [2509.22326].

Within this literature, the term therefore refers both to a specific dataset name and to a sensing formulation: paired radio reflections and reference PPG, collected under controlled conditions, then processed as a cross-modal regression or translation problem. A common misconception is to treat Radio-PPG as a direct optical PPG measurement performed at a distance. The source papers instead describe synthetic or reconstructed PPG waveforms inferred from radio observations, with contact PPG retained as ground truth [2402.14565].

## 2. Original Radio-PPG-16 acquisition protocol

The original dataset, explicitly named **Radio-PPG-16**, comprises **16 healthy young volunteers**, with **10 male, 6 female**, and an **age range of 25 to 32 years** [2402.14565]. Each subject participated in **two measurement sessions**, each session lasted **5 minutes**, and the **total duration per subject** was **10 minutes**. The resulting **total dataset length** was **160 minutes**.

The acquisition protocol was deliberately controlled. The subject sits **still** about **35 cm** from the SDR transmitter-receiver pair in order to avoid motion artifacts. During recording, **radio data** and **reference PPG data** are captured **simultaneously**. The radio data are reflected signals off the chest, and the reference PPG is recorded with the subject’s finger placed on the **MAX86150 PPG sensor**. All procedures were approved by the **ethical institutional review board (EIRB)** at KAUST, Saudi Arabia [2402.14565].

The radio instrumentation consisted of **two USRP N210 SDRs**, one transmitter and one receiver, each connected to a workstation via Ethernet and each attached to a **directional horn antenna**, forming a **SISO** setup. The transmitter generated an **OFDM signal** with **64 sub-carriers**, **QPSK per sub-carrier**, and a **cyclic prefix of 16 samples** appended to each symbol. The transmitter antenna gain was **40 dB**. The receiver removed the cyclic prefix and logged the **complex-valued time-domain data**. The **center frequency** was **5.24 GHz**, the **baseband sampling rate** was **20,000 samples/sec (20 kHz)**, and the **bandwidth** was **20 kHz**. The reference **MAX86150 module** sampled the PPG signal at **2.5 kHz** [2402.14565].

The recorded formats reflect the cross-modal nature of the dataset. **Radio data** are **complex-valued time sequences, recorded at 20 kHz**, and **PPG data** are **real-valued, recorded at 2.5 kHz**. Each session captures **raw radio data** and a **reference PPG waveform**; the detailed dataset description specifies **64 channels/sub-carriers; data from 16 used** [2402.14565].

## 3. Signal processing, alignment, and regression in the original dataset

The original processing pipeline is specified stepwise and combines denoising, dimensionality reduction, normalization, segmentation, and synchronization before regression [2402.14565]. For the reference PPG data, the sequence is: **manual artifact removal**, **wavelet denoising (db2 family)** for baseline drift estimation and removal, a **12th order Butterworth low-pass filter (cutoff 3.4 Hz)**, **Z-score normalization**, and **segmentation** into **2.2-second non-overlapping segments**. The normalization is given as

$$
x_{z} = \frac{x - \mu}{\sigma}
$$

where \(x\) is the sample, \(\mu\) is mean, and \(\sigma\) is standard deviation [2402.14565].

For the radio data, the preprocessing begins with **sub-carrier selection: 16 equally spaced out of 64 sub-carriers**. Principal component analysis is then applied separately to the real and imaginary parts of the complex-valued radio matrix \(X \in \mathbb{C}^{16 \times N}\):

$$
\begin{align*}
&X_R = \text{Re}(X),\quad X_I = \text{Im}(X) \\
&\hat{X}_R = \text{PCA}_1(X_R),\quad \hat{X}_I = \text{PCA}_1(X_I)
\end{align*}
$$

The outputs are concatenated as \(\hat{X} = [\hat{X}_R; \hat{X}_I] \in \mathbb{R}^{2N}\), followed by taking the modulus \(|\hat{X}|\). Denoising is then performed via **Discrete Wavelet Transform (DWT)** with **10-level decomposition**, zeroing coefficients corresponding to non-blood-volume changes in wavelet reconstruction, followed by **Z-score normalization** and **2.2-second non-overlapping segmentation** [2402.14565].

Synchronization is a central element because simultaneous recording alone does not guarantee peak-to-peak alignment. The stated purpose of the cross-correlation stage is to achieve **peak-to-peak alignment between radio and PPG segments, minimizing phase mismatch**. The method computes

$$
R_{xy}(\tau) = \sum_{n} x[n]\, y[n + \tau]
$$

where \(x[n]\) is the radio segment and \(y[n]\) is the PPG segment. The time shift \(\tau\) that **maximizes** \(R_{xy}(\tau)\) is used for alignment. The paper describes this as ensuring that the morphological peaks, i.e., heart beats, are aligned across modalities [2402.14565].

After synchronization, the study proceeds to waveform translation. Both the radio and PPG segments are converted into the frequency domain using **discrete cosine transform (DCT)**, and a **non-linear regression** is learned between them. The synthetic PPG signal is reconstructed by taking the **inverse DCT** of the regression output. The reported reconstruction performance is a **mean absolute error of 8.1294** [2402.14565]. The authors further state that the synthetic PPG waveform preserves clinically significant features, including heart rate and waveform morphology; this suggests that the dataset was designed not merely for scalar vital-sign estimation but for waveform-level inference.

## 4. Expanded Radio-PPG dataset and digital twin formulation

A later paper uses the same dataset name, **Radio-PPG**, for a larger collection designed for **photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals** [2509.22326]. In this version, the dataset consists of **300 minutes worth of near synchronous 64-channel radio data, PPG data, along with the labels (three body vitals) of 30 healthy subjects**. The cohort comprises **30 healthy young adults**, specifically **15 males aged 22–36** and **15 females aged 22–32**, and each subject again contributes **2 sessions of 5 minutes each**, yielding **300 minutes total**. The study was approved by the **Institutional Biosafety and Bioethics Committee, KAUST, Protocol 23IBEC002**, and all subjects gave written consent [2509.22326].

The hardware remains SDR-centric. The setup uses **two USRP N210 SDRs**, each connected to a PC and a directional horn antenna. One horn antenna transmits a **6G/WiFi-like OFDM signal** toward the subject’s chest and the other receives the reflected signal. The **carrier frequency** is **5.23 GHz**, the **bandwidth** is **200 kHz**, and the **sample rate** is **20,000 samples/sec (20 kHz)**. The radio modality is the raw **64-channel complex-valued OFDM channel frequency responses (CFR)**. Reference PPG is acquired with a **MAX86150 module**, specifically the **red-channel photoplethysmogram**, sampled at **200 Hz**. The three physiological labels—**heart rate, SpO\(_2\), respiratory rate**—are acquired with a **Massimo pulse oximeter**, sampled at **1 Hz** [2509.22326].

The later synchronization method is described as **template matching optimization**. The lag \(T\) is chosen to maximize the inner product between the radio-derived waveform \(w(t)\) and the reference PPG \(y(t-T)\):

$$
\underset{T}{\max} \left( \int y(t-T) w(t) \, dt \right)
$$

This lag is then used to time-align paired signal segments [2509.22326]. The corresponding preprocessing pipeline differs from the original one in several details. The OFDM data are reduced from **64 to 16 non-redundant channels**, each complex channel is split into real and imaginary parts to yield a **32-channel real-valued tensor**, the signals are segmented into **2.5-second non-overlapping windows**, and **per-channel z-score normalization** is applied. For robustness, **Gaussian noise** with \(\mu = 0\) and \(\sigma^2 = 0.01\) is added as data augmentation. The reference PPG is **detrended via wavelet decomposition (db2 family)**, denoised with a **12th-order lowpass Butterworth filter, 4 Hz cutoff**, segmented into **2.5 s** windows, normalized by Z-score, and similarly augmented [2509.22326].

Two model families are evaluated. The first is **DCT + MLP**, in which radio and PPG segments are converted to **DCT type-II** coefficients and a **5-layer MLP** learns the mapping. The second is a **custom 1D U-NET model** in cascade, consisting of an **Approximation Network** and a **Refinement Network: MultiRes U-NET**. The hybrid loss combines **mean absolute error**, **first derivative error**, and **second derivative error**, with equal weights \(\lambda_1 = \lambda_2 = 1\). The study uses both a **random 80/20 split** and **Leave-Two-Subjects-Out (LTSO)** cross-validation [2509.22326].

The reported main result is that the **U-NET model achieves an impressive relative mean absolute error of 0.194 with a small ISAC sensing overhead of 15.62%, for DT-PPG synthesis** [2509.22326]. The paper also states that the quality assessment of the synthetic DT-PPG, performed via DT-PPG-based vitals estimation and feature extraction, is **at par with that of reference PPG-based vitals estimation and feature extraction**. A plausible implication is that the later Radio-PPG dataset is intended as a benchmark for waveform fidelity, downstream vital-sign estimation, and resource-aware sensing within ISAC systems rather than only for direct waveform regression.

## 5. Dataset variants, labels, and relation to optical non-contact datasets

The name “Radio-PPG dataset” is therefore used for at least two closely related resources with overlapping sensing logic but different scale and labeling profiles.

| Variant | Acquisition profile | Status/use |
|---|---|---|
| Radio-PPG-16 | 16 subjects, 160 minutes, radio + reference PPG | Dataset name given; public sharing not explicitly mentioned |
| Radio-PPG | 30 subjects, 300 minutes, 64-channel radio + PPG + HR/SpO\(_2\)/RR labels | Used for DT-PPG synthesis and downstream validation |

The original Radio-PPG-16 description states that the paper does **not** explicitly mention public sharing, download links, or access instructions [2402.14565]. The later Radio-PPG description provides detailed acquisition and evaluation protocols but, in the supplied record, does not specify a public release mechanism [2509.22326]. As a result, access conditions are less explicit than in several contemporary video-based datasets.

Relative to optical datasets, Radio-PPG occupies a different point in the design space. **DLCN** is a **large-scale, publicly available remote photoplethysmography dataset** with approximately **13 hours of video data** from **98 participants** under **four representative nighttime lighting scenarios**, together with synchronized **PPG, HR, and SpO\(_2\)** [2507.04306]. **LADH** provides **240 synchronized RGB and infrared facial videos** from **21 participants** across **five common personal care scenarios**, with ground-truth **PPG, respiration, and blood oxygen signals**, and is explicitly open with code [2506.09718]. **VideoPulse** is a neonatal facial-video dataset with **157 recordings totaling 2.6 hours from 52 neonates**, containing synchronized reference **PPG, HR, and SpO\(_2\)**, but it is **not publicly downloadable** due to privacy concerns [2602.23771]. **VicarPPG 2** and **CleanerPPG** were introduced for **HR and HRV** evaluation, combining webcam video with synchronized **ECG** and **PPG**, plus hand-cleaned beat annotations [2012.15846].

These comparisons clarify that Radio-PPG is not primarily a variant of facial-video rPPG benchmarking. It is a radio-reflection dataset for cross-modal biosignal synthesis. This distinction matters methodologically: optical datasets emphasize illumination, facial ROI localization, and video-domain augmentation, whereas Radio-PPG emphasizes OFDM channel selection, complex-valued signal handling, synchronization between radio and PPG, and waveform reconstruction from radio observations.

## 6. Research significance, limitations, and interpretive cautions

The original paper attributes **great clinical significance** to the synthetic PPG waveform because it could be used for **non-contact performance assessment of cardiovascular and respiratory systems** of patients suffering from **infectious diseases**, for example **covid19** [2402.14565]. The later paper similarly frames the dataset as a **foundational step** toward non-contact screening tools for **covid-19**, **cardiovascular diseases**, and **well-being assessment of people with special needs** [2509.22326]. Across both formulations, the proposed benefits include **continuous, seamless, and in-situ health monitoring**, support for **remote healthcare solutions**, and a modality that, according to the authors, **favors privacy over camera-based systems** [2402.14565].

At the same time, the dataset family has explicit constraints. Both main versions were collected from **healthy** subjects, and the acquisition protocols instructed participants to sit **still** to avoid motion artifacts [2402.14565; 2509.22326]. The original dataset is limited to **16 healthy young subjects**, while the later version expands to **30 healthy subjects** but remains within a young-adult cohort. This suggests that cross-population robustness, pathological variability, and performance under unconstrained motion remain open questions rather than established properties of the dataset. The later paper itself identifies future prospects in **larger/more diverse cohorts**, **pathological conditions**, and exploration of **sex, distance, and environmental variables** [2509.22326].

Another interpretive caution concerns the meaning of “PPG acquisition.” In these papers, the non-contact signal is not a directly sensed optical PPG but a radio-derived synthetic waveform trained or aligned against a contact reference PPG. That distinction is central for evaluation: the relevant target is morphological and physiological agreement with the reference waveform, not merely detection of mean heart rate. The use of DCT-domain regression in the original work and U-NET-based time-domain synthesis with derivative-aware loss in the later work underscores that Radio-PPG is best understood as a paired cross-modal waveform dataset for biosignal translation, digital twinning, and downstream vitals inference rather than as a conventional scalar-label vital-sign corpus [2402.14565; 2509.22326].

Source: https://www.emergentmind.com/topics/radio-ppg-dataset