README.md

---
license: cc-by-nc-4.0
task_categories:
  - time-series-forecasting
  - other
language:
  - en
tags:
  - ppg
  - photoplethysmography
  - heart-rate
  - ecg
  - physiological-signals
  - biosignals
  - wearable
  - multisite
  - earring
  - ring
  - smartwatch
pretty_name: Multisite PPG Dataset
size_categories:
  - 10G<n<100G
---

# Multisite PPG Dataset

A multisite photoplethysmography (PPG) dataset: long-duration recordings from four body locations, synchronized activity logs, and ECG-derived heart-rate ground truth. It supports PPG-based HR estimation, signal-quality assessment, motion-artifact handling, and cross-site generalization research.

For data preprocessing and baseline training code, see our GitHub repository: [anonymous-ppg/wearable-ppg-dataset](https://github.com/anonymous-ppg/wearable-ppg-dataset).


---

## At a glance

| | |
| --- | --- |
| **Approx. size** | ~18.6 GB |
| **Participants** | 20 (`P1`–`P20`) |
| **Setting** | Multi-day, free-living |
| **Wearable sites** | Earring, Necklace, Ring, Watch |
| **Wearable modalities** | Green PPG, IR PPG, 3-axis accelerometer, skin temperature |
| **References** | Polar H10 ECG + 3-axis accelerometer |
| **HR ground truth** | From synchronized Polar ECG (Pan–Tompkins + additional cleaning) |

### Sampling rates

| Source | Signals | Rate |
| --- | --- | ---: |
| Wearable | PPG, accelerometer, temperature | **100 Hz** |
| Polar | ECG | **130 Hz** |
| Polar | Accelerometer | **25 Hz** |

---

## Repository layout

Three main components ship in this repo:

| Path | Role |
| --- | --- |
| `raw_data/` | Raw wearable streams, Polar references, activity logs |
| `ppg_windowed_data/` | 8 s windows aligned with ECG-derived HR labels |
| `sample_data/` | Tiny subset for format checks / tutorials |

Top-level tree:

```text
Multisite-PPG/
├── raw_data/
├── ppg_windowed_data/
├── sample_data/
├── .gitattributes
└── croissant.json          # ML Croissant metadata
```

---

## raw_data/

One folder per participant: wearable files at four sites, Polar references, and an activity log.

### Wearable: `P<N>_<Site>_raw.npz`

`<Site>` ∈ {`Earring`, `Necklace`, `Ring`, `Watch`}.

Each file holds timestamped green + IR PPG, 3-axis accelerometer, skin temperature, and alignment metadata for Polar.

### Polar references

| File | Content |
| --- | --- |
| `P<N>_polar_ecg_raw.npz` | Polar H10 ECG |
| `P<N>_polar_accl_raw.npz` | Polar H10 3-axis accelerometer |

### Activity log

`P<N>_activity_log.txt` — timestamped labels as `start <label>` / `end <label>` pairs.

---

## ppg_windowed_data/

Pre-windowed segments per participant and site, aligned with ECG-derived HR.

```text
ppg_windowed_data/
├── P1/
│   ├── alignment_windows_P1_Earring.npz
│   ├── alignment_windows_P1_Necklace.npz
│   ├── alignment_windows_P1_Ring.npz
│   └── alignment_windows_P1_Watch.npz
├── P2/
│   └── ...
└── P<N>/
    └── ...
```

Each `alignment_windows_*.npz` includes: 8 s wearable PPG windows, motion channels, aligned ECG reference, ECG valid length, and HR labels.

| Parameter | Value |
| --- | ---: |
| Window length | 8 s |
| Stride | 1 s |

---

## sample_data/

Same layout as `ppg_windowed_data/`, but small enough for quick inspection without pulling the full release.
Details in its own readme.

---

## Quick start
### Install
```bash
pip install huggingface_hub numpy
```
### Download dataset
**Sample only** (~1.4 GB):
 
```python
from huggingface_hub import snapshot_download
 
snapshot_download(
    repo_id="anonymous-ppg-dataset/Multisite-PPG",
    repo_type="dataset",
    local_dir="multisite-ppg-submission",
    allow_patterns="sample_data/*",
)
```
 
**Full dataset** (~18.6 GB):
 
```python
from huggingface_hub import snapshot_download
 
snapshot_download(
    repo_id="anonymous-ppg-dataset/Multisite-PPG",
    repo_type="dataset",
    local_dir="multisite-ppg-submission",
)
```

---

## Loading in Python

```python
from pathlib import Path
import numpy as np

# Example file:
# Multisite-PPG/ppg_windowed_data/P1/alignment_windows_P1_Earring.npz
npz_path = Path("Multisite-PPG/ppg_windowed_data/P1/alignment_windows_P1_Earring.npz")

with np.load(npz_path) as z:  # allow_pickle=False by default
    ppg_green = z["ppg_green"]      # (N, T)
    ppg_ir = z["ppg_ir"]            # (N, T)
    hr_gt = z["hr_gt"]              # (N,)
    t0_ms = z["t0_ms"]              # (N,)
    t1_ms = z["t1_ms"]              # (N,)
    ppg_fs = float(z["ppg_fs"])     # scalar, typically 100.0
    ecg = z["ecg"]                  # (N, Lmax), zero-padded
    ecg_valid_len = z["ecg_valid_len"]  # (N,), valid ECG samples per window
    accel_x = z["accel_x"]          # (N, T)
    accel_y = z["accel_y"]          # (N, T)
    accel_z = z["accel_z"]          # (N, T)

print("ppg_green:", ppg_green.shape, "hr_gt:", hr_gt.shape, "ppg_fs:", ppg_fs)

# Example: recover valid (unpadded) ECG for one window
i = 0
ecg_i = ecg[i, : int(ecg_valid_len[i])]
```

- Use `ppg_green` or `ppg_ir` as model input windows.
- Use `hr_gt` as the heart-rate label aligned to each window.
- Use `ecg_valid_len` to remove right-side zero padding from `ecg`.

---

## Intended uses

- PPG-based heart-rate estimation under motion  
- Signal-quality assessment and artifact detection  
- Cross-site generalization and multisite wearable modeling  
- Benchmarking preprocessing and representation learning on biosignals  

---

## Limitations

- Free-living recordings: not strictly gap-free continuous sessions.  
- Motion artifacts and low-quality segments occur naturally.  
- Activity labels are user-logged and sparse, not exhaustive.  
- **Not** validated for clinical decision support.  

---

## Ethics and anonymization

Participant IDs are replaced with codes (`P1` … `P20`). This release does not include personally identifying information.

---

## License

Released under [**CC BY-NC 4.0**](https://creativecommons.org/licenses/by-nc/4.0/) (Attribution–NonCommercial).

**Allowed:** non-commercial research and education; derivative preprocessing with attribution.

**Not allowed without separate permission:** commercial use; re-identification attempts; clinical deployment without independent validation.

Full legal text: [Creative Commons BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode).

---

## Extra metadata

Machine-readable dataset description: see [`croissant.json`](https://mlcommons.org/croissant/) at the repository root.

---