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<!DOCTYPE html>
<html class="no-js" lang="en">
<head>
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<title>Website of Philip Gouverneur</title>
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<a href="#education">Publications</a>
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<span class="site-title">Philip Gouverneur</span>
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<span class="site-description">Data Scientist | Researcher | ML Engineer</span>
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<h2 class="heading">About Me</h2>
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loading="lazy">
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<div class="col-md-9">
<p class="about-text">
I am Philip Gouverneur, and I hold a PhD in Computer Science. My studies at the University of
Lübeck had a strong focus on machine learning (ML) and its applications, especially in time
series analysis and deep learning (DL). My research journey has led me to explore the exciting
field of Explainable AI (XAI). I've had the privilege of applying these concepts to real-world
projects. One notable project was my involvement in a BMBF-funded initiative called PainMonit,
which focused on automated pain detection based on physiological sensing modalities. The results
of my research have been reported in several publications, mainly in the journal MDPI. Since of
May 2024, I am Data Scientist at Celver. In my spare time, I enjoy photography, skiing, and
playing the guitar.
</p>
</div>
</div>
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<!-- End #about -->
<div id="experience" class="background-alt">
<h2 class="heading">Experience</h2>
<div id="experience-timeline">
<div data-date="January 2025 - Now" class="hiddenScroll" style="--order: 1;">
<div class="experience-container">
<div class="experience-img">
<a href="https://www.celver.com/" target="_blank">
<img class="publication-image" src="images/logos/celver.webp" loading="lazy"
alt="Celver Logo" title="Celver Logo">
</a>
</div>
<div>
<h3>celver</h3>
<h4>Senior Data Scientist</h4>
<p>
Since January 2025 I am working as a Senior Data Scientist at celver AG. Building on my
previous experience within the Data Science team, I now take on a more strategic and leading
role in designing and implementing advanced analytics solutions for our clients. My work
focuses on leveraging AI and machine learning to enhance our Smart Analytics and Smart Data
offerings.
</p>
</div>
</div>
</div>
<div data-date="May 2024 - December 2024" class="hiddenScroll" style="--order: 2;">
<div class="experience-container">
<div class="experience-img">
<a href="https://www.celver.com/" target="_blank">
<img class="publication-image" src="images/logos/celver.webp" loading="lazy"
alt="Celver Logo" title="Celver Logo">
</a>
</div>
<div>
<h3>celver</h3>
<h4>Data Scientist</h4>
<p>
I was working as a Data Scientist at celver AG. We support companies in
developing a holistic management model with innovative solutions and digitalising the
relevant analysis and planning processes. To get there, we use different solution
strategies, such as Smart Planning, Smart Analytics, Smart Data and Smart Cloud. I myself am
part of the Data Science team.
</p>
</div>
</div>
</div>
<div data-date="March 2019 - March 2024" class="hiddenScroll" style="--order: 3;">
<div class="experience-container">
<div class="experience-img">
<img class="publication-image" src="images/logos/uni-luebeck.webp" loading="lazy"
alt="Uni Luebeck Logo" title="Uni Luebeck Logo">
</div>
<div>
<h3>University of Lübeck</h3>
<h4>PhD student</h4>
<p>
I was a PhD student at the University of Lübeck, studying computer science with a
focus on medical applications. I have been mainly working on a BMBF project called
<i>PainMonit</i> with the aim of building automated pain recognition systems based on
physiological sensor modalities.
</p>
<p>Thesis title: "Machine Learning Methods for Pain Investigation Using Physiological Signals"
</p>
</div>
</div>
</div>
<div data-date="October 2016 - March 2019" class="hiddenScroll" style="--order: 4;">
<div class="experience-container">
<div class="experience-img">
<img class="publication-image" src="images/logos/uni-siegen.webp" loading="lazy"
alt="Uni Siegen Logo" title="Uni Siegen Logo">
</div>
<div>
<h3>University of Siegen</h3>
<h4>Master of Science</h4>
<p><b>Computer Science with Medical Application</b></p>
<p>Thesis title: "Attention-based Approaches for Interpretability of CNN Models for Human
Activity Recognition"</p>
</div>
</div>
</div>
<div data-date="October 2016 - March 2019" class="hiddenScroll" style="--order: 5;">
<div class="experience-container">
<div class="experience-img">
<img class="publication-image" src="images/logos/uni-siegen.webp" loading="lazy"
alt="Uni Siegen Logo" title="Uni Siegen Logo">
</div>
<div>
<h3>University of Siegen</h3>
<h4>Student Assistant</h4>
<p>
I worked as a student assistant in the Pattern Recognition Group at the University of
Siegen. My work included the lead of the WP6 "Data Fusion, Analytics and other Services",
organisation and implementation of a DSS in the frame of the Horizon 2020 project "my-AHA".
</p>
</div>
</div>
</div>
<div data-date="October 2013 - October 2016" class="hiddenScroll" style="--order: 6;">
<div class="experience-container">
<div class="experience-img">
<img class="publication-image" src="images/logos/uni-siegen.webp" loading="lazy"
alt="Uni Siegen Logo" title="Uni Siegen Logo">
</div>
<div>
<h3>University of Siegen</h3>
<h4>Bachelor of Science</h4>
<p><b>Computer Science with Medical Application</b></p>
<p>Thesis title: "Classification of physiological data for emotion recognition"</p>
</div>
</div>
</div>
</div>
</div>
<!-- End #experience -->
<!-- Publications -->
<div id="education">
<h2 class="heading">Selected Publications</h2>
<!-- Start publication-block -->
<div class="education-block shadow-large hiddenScroll" style="--order: 1;">
<span class="education-date">27 September 2024</span>
<div class="education-container">
<h3>The PainMonit Database: An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition</h3>
<h4>Scientific Data</h4>
<div class="publication-image-container">
<img class="publication-image" src="images/publications/devices.webp" loading="lazy"
alt="Philip Gouverneur Scientific Data Publication 2024" title="Scheme of Sensors Publication 2024">
</div>
<div class="publication-text">
Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.
</div>
<a href="#cite_scientific_data" class="btn-rounded-white modal-button">Cite</a>
<a href="./painmonit.html" class="btn-rounded-white">Download page</a>
</div>
</div>
<!-- End publication-block -->
<!-- Start publication-block -->
<div class="education-block shadow-large hiddenScroll" style="--order: 2;">
<span class="education-date">9 February 2023</span>
<div class="education-container">
<h3>Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of
Electrodermal Activity for Automated Pain Recognition</h3>
<h4>MDPI Sensors</h4>
<div class="publication-image-container">
<img class="publication-image" src="images/publications/xai.webp" loading="lazy"
alt="Philip Gouverneur Sensors Publication 2023" title="Scheme of Sensors Publication 2023">
</div>
<div class="publication-text">
Artificial intelligence and especially deep learning methods have achieved outstanding
results for various applications in the past few years. Pain recognition is one of them,
as various models have been proposed to replace the previous gold standard with an
automated and objective assessment. While the accuracy of such models could be increased
incrementally, the understandability and transparency of these systems have not been the
main focus of the research community thus far. Thus, in this work, several outcomes and
insights of explainable artificial intelligence applied to the electrodermal activity
sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this
purpose, the importance of hand-crafted features is evaluated using recursive feature
elimination based on impurity scores in Random Forest (RF) models. Additionally,
Gradient-weighted class activation mapping is applied to highlight the most impactful
features learned by deep learning models. Our studies highlight the following insights:
(1) Very simple hand-crafted features can yield comparative performances to deep
learning models for pain recognition, especially when properly selected with recursive
feature elimination. Thus, the use of complex neural networks should be questioned in
pain recognition, especially considering their computational costs; and (2) both
traditional feature engineering and deep feature learning approaches rely on simple
characteristics of the input time-series data to make their decision in the context of
automated pain recognition.
</div>
<a href="https://www.mdpi.com/1424-8220/23/4/1959" target="_blank" class="btn-rounded-white">Link</a>
<a href="#cite_xai" class="btn-rounded-white modal-button">Cite</a>
<a href="https://www.mdpi.com/1424-8220/23/4/1959/pdf?version=1676027846" class="btn-rounded-white">
<i class="icon fa fa fa-file-pdf-o" aria-hidden="true"></i>
</a>
<!-- Cite Modal -->
<div id="cite_xai" class="modal">
<!-- Modal content -->
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<div class="modal-header">
<span class="close">×</span>
<div class="modal-heading">Cite: Explainable Artificial Intelligence (XAI) in Pain Research:
Understanding the Role of Electrodermal Activity for Automated Pain Recognition</div>
</div>
<div class="modal-body">@article{gouverneur2023explainable,
title = {Explainable artificial intelligence (XAI) in pain research: Understanding the role of electrodermal activity for automated pain recognition},
author = {Gouverneur, Philip and Li, Fr{\'e}d{\'e}ric and Shirahama, Kimiaki and Luebke, Luisa and Adamczyk, Wac{\l}aw M and Szikszay, Tibor M and Luedtke, Kerstin and Grzegorzek, Marcin},
journal = {Sensors},
volume = {23},
number = {4},
pages = {1959},
year = {2023},
publisher = {MDPI}
}
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<div class="education-block shadow-large hiddenScroll" style="--order: 3;">
<span class="education-date">15 July 2021</span>
<div class="education-container">
<h3>
Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition
</h3>
<h4>MDPI Sensors</h4>
<div class="publication-image-container">
<img class="publication-image" src="images/publications/comparison.webp" loading="lazy"
alt="Philip Gouverneur Sensors Publication 2021" title="Scheme of Sensors Publication 2021">
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<div class="publication-text">
While even the most common definition of pain is under debate, pain assessment has remained the
same for decades. But the paramount importance of precise pain management for successful
healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches
have proposed automatic pain evaluation systems using machine learning models trained with data
coming from behavioural or physiological sensors. Although yielding promising results, machine
learning studies for sensor-based pain recognition remain scattered and not necessarily easy to
compare to each other. In particular, the important process of extracting features is usually
optimised towards specific datasets. We thus introduce a comparison of feature extraction
methods for pain recognition based on physiological sensors in this paper. In addition, the
PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for
heat-induced pain in 52 subjects, is introduced. In total, five different approaches, including
techniques based on feature engineering and feature learning with deep learning, are evaluated on
the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature
engineering approaches can still compete with deep learning approaches in terms of performance.
(2) More complex deep learning architectures do not yield better performance compared to simpler
ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based
annotations to build a robust pain recognition system.
</div>
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<div class="modal-body">@article{gouverneur2021comparison,
title={Comparison of feature extraction methods for physiological signals for heat-based pain recognition},
author={Gouverneur, Philip and Li, Fr{\'e}d{\'e}ric and Adamczyk, Wac{\l}aw M and Szikszay, Tibor M and Luedtke, Kerstin and Grzegorzek, Marcin},
journal={Sensors},
volume={21},
number={14},
pages={4838},
year={2021},
publisher={MDPI}
}
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<h2 class="heading" style="margin-top: 50px;">All Publications</h2>
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<h3>2025</h3>
<li-pubs>
<b>
eVAS: A user-friendly electronic Visual Analogue Scale</b>
<div>
<i>Journal of Open Source Software</i>, 10.107, 6876.
</div>
<div>
<a href="https://doi.org/10.21105/joss.06876" target="_blank"
class="btn-rounded-blue">Link</a>
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<div class="modal-heading">Cite: A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing</div>
</div>
<div class="modal-body">@article{gouverneur2025evas,
title={eVAS: A user-friendly electronic Visual Analogue Scale},
author={Gouverneur, Philip and Li, Fr{\'e}d{\'e}ric and Luebke, Luisa and Szikszay, Tibor M and Roelen, Sonja Dana and Krajewski, Jarek and Luedtke, Kerstin and Grzegorzek, Marcin},
journal={Journal of Open Source Software},
volume={10},
number={107},
pages={6876},
year={2025}
}
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<li-pubs>
<b>
A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing</b>
<div>
<i>PloS one</i>, 3, 20.
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<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0315343" target="_blank"
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<div class="modal-body">@article{hasan2025comprehensive,
title={A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing},
author={Hasan, Md Abid and Li, Fr{\'e}d{\'e}ric and Gouverneur, Philip and Piet, Artur and Grzegorzek, Marcin},
journal={PloS one},
volume={20},
number={3},
pages={e0315343},
year={2025},
publisher={Public Library of Science San Francisco, CA USA}
}
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<h3>2024</h3>
<!-- Publication -->
<li-pubs>
<b>
An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition</b>
<div>
<i>Scientific Data</i>, 11, 1051.
</div>
<div>
<a href="https://doi.org/10.1038/s41597-024-03878-w" target="_blank"
class="btn-rounded-blue">Link</a>
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<div class="modal-body">@article{gouverneur2024experimental,
title={An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition},
author={Gouverneur, Philip and Badura, Aleksandra and Li, Fr{\'e}d{\'e}ric and Bie{\'n}kowska, Maria and Luebke, Luisa and Adamczyk, Wac{\l}aw M and Szikszay, Tibor M and My{\'s}liwiec, Andrzej and Luedtke, Kerstin and Grzegorzek, Marcin and others},
journal={Scientific Data},
volume={11},
number={1},
pages={1051},
year={2024},
publisher={Nature Publishing Group UK London}
}
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<!-- Publication List 2023-->
<h3>2023</h3>
<ul-pubs>
<!-- Publication -->
<li-pubs>
<b>
Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of
Electrodermal Activity for Automated Pain Recognition</b>
<div>
<i>Sensors</i>, 23(4), 1959.
</div>
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<a href="https://www.mdpi.com/1424-8220/23/4/1959" target="_blank"
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</li-pubs><br>
<!-- Publication -->
<li-pubs>
<b>
Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy
Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study</b>
<div>
<i>Sensors</i>, 23(19), 8231.
</div>
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<a href="https://www.mdpi.com/1424-8220/23/19/8231" target="_blank"
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with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain
Patients: An Experimental Heat Pain Study</div>
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<div class="modal-body">@article{luebke2023objective,
title={Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study},
author={Luebke, Luisa and Gouverneur, Philip and Szikszay, Tibor M and Adamczyk, Wac{\l}aw M and Luedtke, Kerstin and Grzegorzek, Marcin},
journal={Sensors},
volume={23},
number={19},
pages={8231},
year={2023},
publisher={MDPI}
}</div>
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<b>
Psychological Mechanisms of Offset Analgesia: The Effect of Expectancy Manipulation</b>
<div>
<i>PLOS ONE</i>, 18(1), 1-16.
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Effect of Expectancy Manipulation</div>
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title={Psychological mechanisms of offset analgesia: The effect of expectancy manipulation},
author={Szikszay, Tibor M and Adamczyk, Waclaw M and Panskus, Janina and Heimes, Lotte and David, Carolin and Gouverneur, Philip and Luedtke, Kerstin},
journal={PloS one},
volume={18},
number={1},
pages={e0280579},
year={2023},
publisher={Public Library of Science San Francisco, CA USA}
}
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<!-- Publication -->
<li-pubs>
<b>
Differential Effects of Thermal Stimuli in Eliciting Temporal Contrast Enhancement: A
Psychophysical Study</b>
<div>
<i>The Journal of Pain</i>, Elsevier.
</div>
<div>
<a href="https://www.sciencedirect.com/science/article/abs/pii/S1526590023005072"
target="_blank" class="btn-rounded-blue">Link</a>
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title={Differential Effects of Thermal Stimuli in Eliciting Temporal Contrast Enhancement: A Psychophysical Study},
author={Luebke, Luisa and von Selle, Janne and Adamczyk, Wac{\l}aw M and Knorr, Moritz J and Carvalho, Gabriela F and Gouverneur, Philip and Luedtke, Kerstin and Szikszay, Tibor M},
journal={The Journal of Pain},
year={2023},
publisher={Elsevier}
}
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<li-pubs>
<b>
Exploring the Benefits of Time Series Data Augmentation for Wearable Human Activity
Recognition</b>
<div>
<i>iWOAR '23</i>, 20, 1-7.
</div>
<div>
<a href="https://dl.acm.org/doi/10.1145/3615834.3615842" target="_blank"
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Augmentation for Wearable Human Activity Recognition</div>
</div>
<div class="modal-body">@inproceedings{hasan2023exploring,
title = {{Exploring the Benefits of Time Series Data Augmentation for Wearable Human Activity Recognition}},
author = {Hasan, Md Abid and Li, Fr{\'e}d{\'e}ric and Piet, Artur and Gouverneur, Philip and Irshad, Muhammad Tausif and Grzegorzek, Marcin},
booktitle = {Proceedings of the 8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence},
pages = {1--7},
year = {2023},
publisher = {Association for Computing Machinery},
doi = {https://doi.org/10.1145/3615834.3615842},
note = {DOI: \href{https://doi.org/10.1145/3615834.3615842}{10.1145/3615834.3615842}},
series = {iWOAR '23},
}
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<h3>2022</h3>
<ul-pubs>
<!-- Publication -->
<li-pubs>
<b>
SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors</b>
<div>
<i>Sensors</i>, 22(20), 7711.
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title={SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors},
author={Irshad, Muhammad Tausif and Nisar, Muhammad Adeel and Huang, Xinyu and Hartz, Jana and Flak, Olaf and Li, Fr{\'e}d{\'e}ric and Gouverneur, Philip and Piet, Artur and Oltmanns, Kerstin M and Grzegorzek, Marcin},
journal={Sensors},
volume={22},
number={20},
pages={7711},
year={2022},
publisher={MDPI}
}
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<li-pubs>
<b>
Temporal Properties of Pain Contrast Enhancement using Repetitive Stimulation</b>
<div>
<i>European Journal of Pain</i>, Wiley Online Library.
</div>
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title={Temporal properties of pain contrast enhancement using repetitive stimulation},
author={Szikszay, Tibor M and Adamczyk, Waclaw M and L{\'e}v{\'e}nez, Juliette LM and Gouverneur, Philip and Luedtke, Kerstin},
journal={European Journal of Pain},
volume={26},
number={7},
pages={1437--1447},
year={2022},
publisher={Wiley Online Library}
}
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</li-pubs><br>
<!-- Publication -->
<li-pubs>
<b>
To Calibrate or not to Calibrate? A Methodological Dilemma in Experimental Pain Research</b>
<div>
<i>The Journal of Pain</i>, Elsevier.
</div>
<div>
<a href="https://www.sciencedirect.com/science/article/pii/S1526590022003662" target="_blank"
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<div class="modal-body">@article{adamczyk2022calibrate,
title={To calibrate or not to calibrate? A methodological dilemma in experimental pain research},
author={Adamczyk, Waclaw M and Szikszay, Tibor M and Nahman-Averbuch, Hadas and Skalski, Jacek and Nastaj, Jakub and Gouverneur, Philip and Luedtke, Kerstin},
journal={The Journal of Pain},
volume={23},
number={11},
pages={1823--1832},
year={2022},
publisher={Elsevier}
}
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</ul-pubs><!-- Publication List 2021-->
<h3>2021</h3>
<ul-pubs>
<!-- Publication -->
<li-pubs>
<b>
Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain
Recognition</b>
<div>
<i>Sensors</i>, 21(14), 4838.
</div>
<div>
<a href="https://www.mdpi.com/1424-8220/21/14/4838" target="_blank"
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</li-pubs><br>
<!-- Publication -->
<li-pubs>
<b>
Classification of Heat-Induced Pain Using Physiological Signals</b>
<div>
<i>Information Technology in Biomedicine</i>, 239-251.
</div>
<div>
<a href="https://link.springer.com/chapter/10.1007/978-3-030-49666-1_19" target="_blank"
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Physiological Signals</div>
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<div class="modal-body">@article{gouverneur2021classification,
title={Classification of Heat-Induced Pain Using Physiological Signals},
author={Gouverneur, Philip J and Li, Fr{\'e}d{\'e}ric and M. Szikszay, Tibor and M. Adamczyk, Waclaw and Luedtke, Kerstin and Grzegorzek, Marcin},
journal={Information Technology in Biomedicine},
pages={239--251},
year={2021},
publisher={Springer}
}
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<h3>2020</h3>
<ul-pubs>
<!-- Publication -->
<li-pubs>
<b>
AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature
Review</b>
<div>
<i>Sensors</i>, 20(18), 5321.
</div>
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General Movements: A Systematic Literature Review</div>
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<div class="modal-body">@article{irshad2020ai,
title={Ai approaches towards Prechtl's assessment of general movements: A systematic literature review},
author={Irshad, Muhammad Tausif and Nisar, Muhammad Adeel and Gouverneur, Philip and Rapp, Marion and Grzegorzek, Marcin},
journal={Sensors},
volume={20},
number={18},
pages={5321},
year={2020},
publisher={MDPI}
}
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</div>
</div>
</div>
</li-pubs><br>
<!-- Publication -->
<li-pubs>
<b>
My-AHA: Software Platform to Promote Active and Healthy Ageing</b>
<div>
<i>Information</i>, 11(9), 438.
</div>
<div>
<a href="https://www.mdpi.com/2078-2489/11/9/438" target="_blank"
class="btn-rounded-blue">Link</a>