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

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
no
no
no
no
no
no
no
no
no
no

0 to 10 of 11

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.

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download
1 citations
8505 views

Creators

Attila Reiss

Ina Indlekofer

Philip Schmidt

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy