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