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<!doctype html>
<html>
<head>
<link rel="stylesheet" href="usi.css" />
<link rel="shortcut icon" type="image/x-icon" href="favicon.ico" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no"
/>
<meta charset="utf-8" />
</head>
<body>
<div id="hdr"></div>
<script async src="hdr.js" strng="Datasets"></script>
<div id="usi_page">
<p>
The following data sets have been recorded during our research and are
being made available for download*:
</p>
<a href="data_lorawanindoor.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/lorawan-indoor.jpg" />
</div>
<div class="lstEntry">
<h3>LoRaWAN-Indoor</h3>
A Comprehensive Data Description for LoRaWAN Path Loss Measurements
in an Indoor Office Setting: Effects of Environmental Factors.
Nahshon Mokua Obiri and Kristof Van Laerhoven. IEEE Access 2025.
<br />
<em
>A dataset collected over 6 months from a multi-room office
environment, using multiple LoRaWAN end devices recording metadata
(RSSI, SNR, spreading factor) alongside synchronized environmental
parameters (temperature, humidity, pressure, CO<sub>2</sub>, and
PM2.5).</em
>
</div>
</div></a
>
<a href="data_wear.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/wear.jpg" />
</div>
<div class="lstEntry">
<h3>WEAR</h3>
WEAR: An Outdoor Sports for Wearable and Egocentric Activity
Recognition. Marius Bock, Hilde Kuehne, Kristof Van Laerhoven, and
Michael Moeller. IMWUT 2024.
<br /><br />
<em
>A challenging activity recognition dataset recorded from 22
participants, each performing 18 different exercises with a
wearable camera and IMUs on all limbs, to investigate the
complementarity of camera- and inertial-based sensors.</em
>
</div>
</div></a
>
<a href="https://ahoelzemann.github.io/hangtime_har/index.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/hangtime23.jpg" />
</div>
<div class="lstEntry">
<h3>Hang-Time</h3>
Hang-Time HAR: A Benchmark Dataset for Basketball Activity
Recognition Using Wrist-Worn Inertial Sensors. Alexander Hoelzemann,
Julia Lee Romero, Marius Bock, Kristof Van Laerhoven, and Qin Lv.
Sensors 2023.
<br />
<em
>An activity recognition dataset recorded from two basketball
teams (in the USA and Germany) with a total of 24 players with
wrist-worn inertial sensors, during basketball training sessions
and games.</em
>
</div>
</div></a
>
<a href="data_ibsync.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/fcs2022.jpg" />
</div>
<div class="lstEntry">
<h3>IBSync</h3>
IBSync: Intra-body synchronization and implicit contextualization of
wearable devices using artificial ECG landmarks. Florian Wolling and
Kristof Van Laerhoven. Frontiers in Computer Science 2022.
<br /><br />
<em
>Recorded measurements of the paper's three conducted
experiments.</em
>
</div>
</div></a
>
<a href="data_embc21.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/embc21.jpg" />
</div>
<div class="lstEntry">
<h3>EMBC 2021</h3>
Optimal Preprocessing of Raw Signals from Reflective Mode
Photoplethysmography in Wearable Devices. Florian Wolling, Sudam
Maduranga Wasala, and Kristof Van Laerhoven. EMBC 2021, Virtual
Event, IEEE, 2021.
<br /><br />
<em
>Supplementary annotations for a large dataset of raw, reflective
mode photoplethysmography.</em
>
</div>
</div></a
>
<a href="data_fcs21.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/fcs2021.jpg" />
</div>
<div class="lstEntry">
<h3>Breathing In-Depth</h3>
Breathing In-Depth: A Parametrization Study on RGB-D Respiration
Extraction Methods. Jochen Kempfle and Kristof Van Laerhoven.
Frontiers 2021.
<br /><br />
<em
>A benchmark dataset with depth data from 19 people performing
different activities, to estimate respiration.</em
>
</div>
</div></a
>
<a href="data_data20.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/data20.jpg" />
</div>
<div class="lstEntry">
<h3>DATA 2020</h3>
The Quest for Raw Signals: A Quality Review of Publicly Available
Photoplethysmography Datasets. Florian Wolling and Kristof Van
Laerhoven. DATA 2020, Virtual Event, Japan, ACM, 2020.
<br /><br />
<em
>The raw PPG reference data and the developed analytical tool for
automatic quality reviews.</em
>
</div>
</div></a
>
<a href="data_sar20.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/sar20.jpg" />
</div>
<div class="lstEntry">
<h3>SAR 2020</h3>
Towards Breathing as a Sensing Modality in Depth-Based Activity
Recognition. Jochen Kempfle and Kristof Van Laerhoven. Sensors 2020.
<br /><br />
<em
>A benchmark dataset with depth data from people performing
activities, to estimate respiration.</em
>
</div>
</div></a
>
<a href="data_ppgdalia.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/sensors19.jpg" />
</div>
<div class="lstEntry">
<h3>PPG-DaLiA</h3>
Deep PPG: Large-scale Heart Rate Estimation with Convolutional
Neural Networks. Attila Reiss, Ina Indlekofer, Philip Schmidt, and
Kristof Van Laerhoven. Sensors 2019.
<br /><br />
<em
>A large dataset with a wide range of physical activities,
performed under close to real-life conditions.</em
>
</div>
</div></a
>
<a href="data_wesad.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/icmi18.jpg" />
</div>
<div class="lstEntry">
<h3>WESAD</h3>
Introducing WESAD, a Multimodal Dataset for Wearable Stress and
Affect Detection. Philip Schmidt, Attila Reiss, Robert Duerichen,
Claus Marberger, and Kristof Van Laerhoven. ICMI’18, Boulder,
Colorado, ACM, 2018.
<br /><br />
<em
>Physiological and motion data, recorded from both a wrist- and a
chest-worn device, of 15 subjects during a lab study.</em
>
</div>
</div></a
>
<a href="data_inf18.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/inf18.jpg" />
</div>
<div class="lstEntry">
<h3>IMU Glove</h3>
Real-Time and Embedded Detection of Hand Gestures with an IMU-Based
Glove. Chaithanya Kumar Mummadi, Frederic Philips Peter Leo, Keshav
Deep Verma, Shivaji Kasireddy, Philipp M. Scholl, Jochen Kempfle,
and Kristof Van Laerhoven. ICMI’18, Boulder, in Informatics, 5(2),
2018.
<br />
<em
>Gesture data wit 22 classes, recorded from a a set of
glove-embedded IMUs, of 57 subjects during a lab study.</em
>
</div>
</div></a
>
<a href="data_wetlab.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/ubicomp15.jpg" />
</div>
<div class="lstEntry">
<h3>WetLab</h3>
Wearables in the Wetlab: a Laboratory System for Capturing and
Guiding Experiments. Philipp M. Scholl, Matthias Wille and Kristof
Van Laerhoven, UbiComp'15, Osaka, Japan, ACM, 2015.
<br /><br />
<em
>Original video recordings, activities encoded as subtitles and
acceleration data of the wrist for 22 participants.</em
>
</div>
</div></a
>
<a href="data_ichi14.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/ichi14.jpg" />
</div>
<div class="lstEntry">
<h3>ICHI 2014</h3>
Towards a Benchmark for Wearable Sleep Analysis with Inertial
Wrist-worn Sensing Units, Marko Borazio, Eugen Berlin, Nagihan
Kücükyildiz, Philipp M. Scholl and Kristof Van Laerhoven. ICHI 2014.
IEEE Press.
<br /><br />
<em
>Recorded PSG and inertial data from 42 sleep lab patients and
scripts to visualize all data.</em
>
</div>
</div></a
>
<a href="data_timeuse.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/ah13.jpg" />
</div>
<div class="lstEntry">
<h3>TimeUse</h3>
Time Use Surveys: Improving Activity Recognition without Sensor
Data, Marko Borazio and Kristof Van Laerhoven, ACM AH 2013.
<br /><br />
<em
>Experiment scripts for using time surveys and activity episodes
from 5160 households / 13798 individuals.</em
>
</div>
</div></a
>
<a href="data_leisure.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/ubicomp12.jpg" />
</div>
<div class="lstEntry">
<h3>LeisureActivities</h3>
Detecting Leisure Activities with Dense Motif Discovery, Eugen
Berlin and Kristof Van Laerhoven, Ubicomp 2012. ACM Press.
<br /><br />
<em
>Raw sensor data from 6 participants wearing our wrist-worn
prototype for a full week.</em
>
</div>
</div></a
>
<a href="data_trainspotting.html"
><div class="lstItem">
<div class="lstImg">
<img src="img/d/inss12.png" />
</div>
<div class="lstEntry">
<h3>Trainspotting</h3>
Trainspotting: Combining Fast Features to Enable Detection on
Resource-constrained Sensing Devices, Eugen Berlin and Kristof Van
Laerhoven, INSS 2012, IEEE Press.
<br /><br />
<em
>Vibration footprint data from rail-based wireless sensing nodes,
for 250 trains of different types.</em
>
</div>
</div></a
>
<p>
*Please note that although all our data sets are being published here
without any restrictions (they are free to download, free to use and to
publish about without special requirements from our side) by default,
some of them might be subject to stricter control because of the nature
of the data or project they were funded by. See the individual data
set's descriptions for more information.
</p>
</div>
<div id="usi_ftr"></div>
</body>
</html>