# ubicomplab/rPPG-Toolbox

rPPG-Toolbox: Deep Remote PPG Toolbox (NeurIPS 2023)

Repository: https://github.com/ubicomplab/rPPG-Toolbox
Canonical: https://ross.abutalabs.com/products/rppg-toolbox
Homepage: https://arxiv.org/abs/2210.00716
Language: Python
License: NOASSERTION
License Family: other
Topics: camera, health, mobile-health, physiological-computing, ppg, remote-ppg, rppg, health-sensing, mobilehealth, physiologicalsensing, cardiova, computer-vision, healthcare, deep-learning, healthcare-ai, healthcare-application, mobile-computing, ubiquitous-computing, vital-signs, remote-physiological-measurement
Last push: 2025-09-15T12:37:46+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 42, release rhythm 35, longevity 100
- inputs: {"age_days": 1882, "days_push": 352, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1178, forks 304 (observed 2026-08-28T04:03:52.964741+00:00)

## What it is
rPPG-Toolbox is an open-source Python toolbox for camera-based physiological sensing (remote photoplethysmography), enabling heart rate and blood volume pulse estimation from face videos. It benchmarks state-of-the-art unsupervised and supervised neural rPPG methods and supports rapid development of new algorithms with dataset, augmentation, and evaluation support.

## Use cases
- estimate heart rate from webcam video
- measure blood volume pulse from face recordings
- benchmark rPPG algorithms on public datasets
- develop and evaluate new neural rPPG models
- contactless vital sign monitoring research
- compare unsupervised vs deep learning rPPG methods

## When to choose
- you need a comprehensive, research-grade rPPG codebase with many implemented methods
- you want to benchmark new rPPG models against state-of-the-art baselines
- you are doing academic research in camera-based physiological sensing

## When to avoid
- you need a production-ready, clinically validated vital signs product
- you need a simple end-user app rather than a research toolbox
- you require a permissive license for commercial use (license is non-standard)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, benchmarking
- domain: computer-vision, healthcare, machine-learning, deep-learning
- platform: python, cross-platform
- tags: rppg, remote-physiological-measurement, vital-signs, heart-rate-estimation, camera-based-sensing, health-sensing, benchmark, neurips-2023

## Member repositories
- ubicomplab/rPPG-Toolbox (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:52.964741+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:26:09.146836+00:00, confidence not recorded.
  - readme: https://github.com/ubicomplab/rPPG-Toolbox (fetched 2026-08-28T04:03:52.964741+00:00, sha 2d48b869ea56)
  - homepage: https://arxiv.org/abs/2210.00716 (fetched 2026-08-29T12:33:03.512041+00:00, sha 9cbab1324f97)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T12:33:03.521808+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T12:33:03.525625+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T12:33:03.527721+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T12:33:03.523791+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.