Create README.md

README.md

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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/>
+
+___________
+
+Quick Links: 
+- [Project Website](https://siplab.org/projects/WildPPG)
+- [Paper](https://static.siplab.org/papers/neurips2024-wildppg.pdf)
+----------
+
+# Loading the Dataset
+The dataset is available for download [here](https://huggingface.co/datasets/siplab/WildPPG/tree/main)
+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
+```
+
+