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Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors : Heart Rate Analysis Software from the Taking the Fast Lane Project. / van Gent, Paul; Farah, Haneen; van Nes, N.; van Arem, Bart.

In: Journal of Open Research Software, Vol. 7, No. 1, 32, 2019, p. 1-9.

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@article{99d0b50705754bf5935bc248b82e379e,
title = "Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors: Heart Rate Analysis Software from the Taking the Fast Lane Project.",
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.",
keywords = "Heart rate analysis, Human factors, PPG, Python, Arduino",
author = "{van Gent}, Paul and Haneen Farah and {van Nes}, N. and {van Arem}, Bart",
year = "2019",
doi = "10.5334/jors.241",
language = "English",
volume = "7",
pages = "1--9",
journal = "Journal of Open Research Software",
issn = "2047-9647",
number = "1",

}

RIS

TY - JOUR

T1 - Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors

T2 - Journal of Open Research Software

AU - van Gent, Paul

AU - Farah, Haneen

AU - van Nes, N.

AU - van Arem, Bart

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Heart rate analysis

KW - Human factors

KW - PPG

KW - Python

KW - Arduino

UR - http://www.scopus.com/inward/record.url?scp=85075793161&partnerID=8YFLogxK

U2 - 10.5334/jors.241

DO - 10.5334/jors.241

M3 - Article

VL - 7

SP - 1

EP - 9

JO - Journal of Open Research Software

JF - Journal of Open Research Software

SN - 2047-9647

IS - 1

M1 - 32

ER -

ID: 47328740