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PPG-DaLiA Data Set
Download: Data Folder, Data Set Description

Abstract: 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.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

8300000

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

11

Date Donated

2019-07-30

Associated Tasks:

Regression

Missing Values?

N/A

Number of Web Hits:

4707

Source:

Attila Reiss, Robert Bosch GmbH, Corporate Research, Germany, firstname.lastname '@' de.bosch.com
Ina Indlekofer, Bosch Sensortec GmbH, Germany, firstname.lastname '@' bosch-sensortec.com
Philip Schmidt, Robert Bosch GmbH, Corporate Research, Germany, firstname.lastname '@' de.bosch.com


Data Set 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].


Attribute 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.


Relevant Papers:

[1] Attila Reiss, Ina Indlekofer, Philip Schmidt, and Kristof Van Laerhoven. 2019. Deep PPG: Large-scale Heart Rate Estimation with Convolutional Neural Networks. MDPI Sensors, 19(14).



Citation Request:

You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data. Please acknowledge publication [1].
We recommend to refer to this dataset as PPG-DaLiA, or 'PPG dataset for motion compensation and heart rate estimation in Daily Life Activities'.

Supported By:

 In Collaboration With:
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