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license: mit
language:
- en
tags:
- medical
size_categories:
- 100K<n<1M
---
## [WildPPG](https://siplab.org/projects/WildPPG): A Real-World PPG Dataset of Long Continuous Recordings (NeurIPS 2024 Datasets & Benchmarks)
[Manuel Meier](https://scholar.google.com/citations?user=L6f-xg0AAAAJ), [Berken Utku Demirel](https://scholar.google.com/citations?user=zbgxpdIAAAAJ), [Christian Holz](https://www.christianholz.net)<br/>
[Sensing, Interaction & Perception Lab](https://siplab.org), Department of Computer Science, ETH Zürich, Switzerland <br/>
---
## 📖 Overview
WildPPG provides **216 hours** of continuous physiological recordings from **16 participants** in diverse real-world scenarios.
It is the largest dataset for developing and evaluating wearable heart-rate detection algorithms.
---
- **Participants, Duration & Ground truth for heart rate estimation:**
16 healthy adults; 810 min (~13.5 h) each, 216 h total. Ground truth heart rate: obtained from ECG with Pan–Tompkins and cleaned further during motions.
- **Modalities & Placements:**
PPG, ECG (Lead I), 3-axis accel, skin temp, barometric altitude
@ forehead · sternum · wrist · ankle
- **Contexts & Environments:**
Mobility (walking, hiking, stairs), Stationary (meals, resting), Transit (car, train, cable car, elevator)
Temperatures near 0 °C to full sun; altitudes up to 3 571 m
___________
Quick Links:
- [Project Website](https://siplab.org/projects/WildPPG)
- [Paper](https://static.siplab.org/papers/neurips2024-wildppg.pdf)
----------
# Loading the Dataset
The dataset is split into .mat MATLAB files representing participants and can be loaded with MATLAB.
## Loading the Data
You can load the `.mat` file using either Python or MATLAB:
- **Python**:
Use [`scipy.io.loadmat`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html):
```python
from scipy.io import loadmat
data = loadmat('WildPPG.mat')
```
- **MATLAB**:
Use the built-in `load` function:
```matlab
data = load('WildPPG.mat');
```
The `.mat` file contains **14 cell arrays**, each representing a variable (e.g., `data_altitude_values`, `data_bpm_values`, `data_ppg_wrist`).
Each cell array includes **16 entries**, corresponding to data from 16 individual subjects.
### Example loader
```python
import numpy as np
import scipy.io
def load_domain_data(domain_idx):
"""Loads wrist PPG and heart rate data for a single subject (domain).
Args:
domain_idx (int): Index of the subject (0–15).
Returns:
X (np.ndarray): PPG signal data (n_samples × signal_dim).
y (np.ndarray): Heart rate values (bpm), adjusted to start from 0.
d (np.ndarray): Domain labels (same shape as y), equal to domain_idx.
"""
data_path = 'data/WildPPG/WildPPG.mat'
data_all = scipy.io.loadmat(data_path)
# Load PPG signal and heart rate values
data = data_all['data_ppg_wrist']
data_labels = data_all['data_bpm_values']
domain_idx = int(domain_idx)
X = data[domain_idx, 0]
y = np.squeeze(data_labels[domain_idx][0]).astype(int)
# Mask out invalid samples (e.g., NaNs, infs, and HR < 30 bpm)
mask_Y = y >= 30
mask_X = ~np.isnan(X).any(axis=1) & ~np.isinf(X).any(axis=1)
combined_mask = mask_Y & mask_X
X = X[combined_mask]
y = y[combined_mask] - 30 # Normalize HR: min HR is 30 bpm → range starts from 0
d = np.full(y.shape, domain_idx, dtype=int)
return X, y, d
```
#### Load PPG & Heart Rate Data for a Single Subject
The function `load_domain_data(domain_idx)` loads **wrist PPG signal data** and **heart rate (HR) labels** for a single subject from the `WildPPG.mat` file.
- `domain_idx` ranges from `0` to `15`, each corresponding to one of the 16 subjects.
- The data is preprocessed by:
- Removing invalid samples (NaNs, infs, and HR < 30 bpm)
- Normalizing HR values to start from 0 (by subtracting 30 bpm)
- The function returns:
- `X`: preprocessed PPG signal (shape: `n_samples × signal_dim`)
- `y`: adjusted heart rate labels
- `d`: domain label (same shape as `y`, filled with `domain_idx`)
Example usage:
```python
x, y, d = load_domain_data(3) # Load data for subject 3
```
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