PPG-DaLiA
Donated on 7/29/2019
PPG-DaLiA contains data from 15 subjects wearing physiological and motion sensors, providing a PPG dataset for motion compensation and heart rate estimation in Daily Life Activities.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Computer Science
Associated Tasks
Regression
Feature Type
Real
# Instances
8300000
# Features
-
Dataset Information
Additional Information
PPG-DaLiA is a publicly available dataset for PPG-based heart rate estimation. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects while performing a wide range of activities under close to real-life conditions. The included ECG data provides heart rate ground truth. The included PPG- and 3D-accelerometer data can be used for heart rate estimation, while compensating for motion artefacts. Details can be found in the dataset's readme-file, as well as in [1].
Has Missing Values?
No
Variables Table
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Additional Variable Information
Raw sensor data was recorded with two devices: a chest-worn device (RespiBAN) and a wrist-worn device (Empatica E4). The RespiBAN device provides the following sensor data: electrocardiogram (ECG), respiration, and three-axis acceleration. All signals are sampled at 700 Hz. The Empatica E4 device provides the following sensor data: blood volume pulse (BVP, 64 Hz), electrodermal activity (EDA, 4 Hz), body temperature (4 Hz), and three-axis acceleration (32 Hz). The dataset's readme-file contains all further details with respect to the dataset structure, data format (RespiBAN device, Empatica E4 device, synchronised data), study protocol, ground truth generation, etc.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset ppg_dalia = fetch_ucirepo(id=495) # data (as pandas dataframes) X = ppg_dalia.data.features y = ppg_dalia.data.targets # metadata print(ppg_dalia.metadata) # variable information print(ppg_dalia.variables)
Reiss,Attila, Indlekofer,Ina, and Schmidt,Philip. (2019). PPG-DaLiA. UCI Machine Learning Repository. https://doi.org/10.24432/C53890.
@misc{misc_ppg-dalia_495,
author = {Reiss,Attila, Indlekofer,Ina, and Schmidt,Philip},
title = {{PPG-DaLiA}},
year = {2019},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C53890}
}Creators
Attila Reiss
Ina Indlekofer
Philip Schmidt
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.