Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors: Heart Rate Analysis Software from the Taking the Fast Lane Project

Paul van Gent; Haneen Farah; Nicole van Nes; Bart van Arem

doi:10.5334/jors.241

Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors: Heart Rate Analysis Software from the Taking the Fast Lane Project

Journal of Open Research Software

Volume 7 (2019): Issue 1

By: Paul van Gent , Haneen Farah , Nicole van Nes and Bart van Arem

Open Access

|Oct 2019

Abstract

This paper describes the functioning and development of HeartPy: a heart rate analysis toolkit designed for photoplethysmogram (PPG) data. Most openly available algorithms focus on electrocardiogram (ECG) data, which has very different signal properties and morphology, creating a problem with analysis. ECG-based algorithms generally don’t function well on PPG data, especially noisy PPG data collected in experimental studies. To counter this, we developed HeartPy to be a noise-resistant algorithm that handles PPG data well. It has been implemented in Python and C. Arduino IDE sketches for popular boards (Arduino, Teensy) are available to enable data collection as well. This provides both pc-based and wearable implementations of the software, which allows rapid reuse by researchers looking for a validated heart rate analysis toolkit for use in human factors studies.

Funding statement: Part of the software has been developed within the “Taking the Fast Lane” project, funded by NWO TTW¹, project number 13771.

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Articles in this issue

DOI: https://doi.org/10.5334/jors.241 | Journal eISSN: 2049-9647

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Language: English

Submitted on: Aug 1, 2018

Accepted on: Oct 14, 2019

Published on: Oct 29, 2019

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

heart rate analysis,

photoplethysmogram,

python,

arduino,

PPG

© 2019 Paul van Gent, Haneen Farah, Nicole van Nes, Bart van Arem, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 7 (2019): Issue 1