Photoplethysmography (PPG) is rapidly becoming an important signal in cardiovascular and digital health research due to its ease of acquisition from consumer devices such as smartwatches, pulse oximeters, and smartphone cameras. Beyond heart rate and SpO2, its commercial application has already been demonstrated in the detection of atrial fibrillation, with further applications including hypertension, diabetes and heart failure being under research.
Strengthening the evidence for application of the PPG signal acquired by a smartphone camera requires access to raw, standardised pulse waveforms, which is an ongoing problem for many research groups. Existing tools often lack transparent documentation of the acquisition process, limit access to raw, unprocessed signals, or require payment for access to the signal. These limitations reduce reproducibility and hinder algorithm development, pushing many groups toward building custom setups, which are both resource- and time-intensive.
We developed the OpenPPG, a free, research-oriented iOS mobile application designed to support the scientific community by enabling easy, high-resolution PPG signal acquisition via the smartphone camera and raw data export. The app measures the changes in brightness of the video recording using separate RGB channels as well as combined white-light, displays signal real-time and allows users to mark events on the signal and annotate sessions using customisable metadata forms.
It also allows flexible control over signal preprocessing: users can choose to export fully raw signals, apply optional basic filtering and signal quality assessment, or access extracted features from both the temporal (e.g., HRV-related intervals) and morphological (e.g., waveform shape and amplitude) domains. All outputs can be directly exported in standard CSV format, with full access to each RGB channel.
The OpenPPG fills the gap in the field of free-to-use applications for PPG signal acquisition and empowering researchers to drive innovation in signal processing, machine learning, and digital biomarker discovery.