Lehrgebiet: Theoretische Informatik und künstliche Intelligenz
Büro: 01.214
Labor: 04.105
Telefon: +49 208 88254-806
E-Mail:
Ioannis Iossifidis studierte Physik (Schwerpunkt: theoretische Teilchenphysik) an der Universität Dortmund und promovierte 2006 an der Fakultät für Physik und Astronomie der Ruhr-Universität Bochum.
Am Institut für Neuroinformatik leitete Prof. Dr. Iossifidis die Arbeitsgruppe Autonome Robotik und nahm mit seiner Forschungsgruppe erfolgreich an zahlreichen, vom BmBF und der EU, geförderten Forschungsprojekten aus dem Bereich der künstlichen Intelligenz teil. Seit dem 1. Oktober 2010 arbeitet er an der HRW am Institut Informatik und hält den Lehrstuhl für Theoretische Informatik – Künstliche Intelligenz.
Prof. Dr. Ioannis Iossifidis entwickelt seit über 20 Jahren biologisch inspirierte anthropomorphe, autonome Robotersysteme, die zugleich Teil und Ergebnis seiner Forschung im Bereich der rechnergestützten Neurowissenschaften sind. In diesem Rahmen entwickelte er Modelle zur Informationsverarbeitung im menschlichen Gehirn und wendete diese auf technische Systeme an.
Ausgewiesene Schwerpunkte seiner wissenschaftlichen Arbeit der letzten Jahre sind die Modellierung menschlicher Armbewegungen, der Entwurf von sogenannten «Simulierten Realitäten» zur Simulation und Evaluation der Interaktionen zwischen Mensch, Maschine und Umwelt sowie die Entwicklung von kortikalen exoprothetischen Komponenten. Entwicklung der Theorie und Anwendung von Algorithmen des maschinellen Lernens auf Basis tiefer neuronaler Architekturen bilden das Querschnittsthema seiner Forschung.
Ioannis Iossifidis’ Forschung wurde u.a. mit Fördermitteln im Rahmen großer Förderprojekte des BmBF (NEUROS, MORPHA, LOKI, DESIRE, Bernstein Fokus: Neuronale Grundlagen des Lernens etc.), der DFG («Motor‐parietal cortical neuroprosthesis with somatosensory feedback for restoring hand and arm functions in tetraplegic patients») und der EU (Neural Dynamics – EU (STREP), EUCogII, EUCogIII ) honoriert und gehört zu den Gewinnern der Leitmarktwettbewerbe Gesundheit.NRW und IKT.NRW 2019.
ARBEITS- UND FORSCHUNGSSCHWERPUNKTE
- Computational Neuroscience
- Brain Computer Interfaces
- Entwicklung kortikaler exoprothetischer Komponenten
- Theorie neuronaler Netze
- Modellierung menschlicher Armbewegungen
- Simulierte Realität
WISSENSCHAFTLICHE EINRICHTUNGEN
- Labor mit Verlinkung
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LEHRVERANSTALTUNGEN
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PROJEKTE
- Projekt mit Verlinkung
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WISSENSCHAFTLICHE MITARBEITER*INNEN
Felix Grün
Büro: 02.216 (Campus Bottrop)
Marie Schmidt
Büro: 02.216 (Campus Bottrop)
Aline Xavier Fidencio
Gastwissenschaftlerin
Muhammad Ayaz Hussain
Doktorand
Tim Sziburis
Doktorand
Farhad Rahmat
studentische Hilfskraft
AUSGEWÄHLTE PUBLIKATIONEN
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2022
3.Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis
Deep transfer learning compared to subject-specific models for sEMG decoders Artikel
In: Journal of Neural Engineering, Bd. 19, Nr. 5, 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@article{lehmlerTransferLearningPatientSpecific2021bb,
title = {Deep transfer learning compared to subject-specific models for sEMG decoders},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
editor = {{IOP Publishing},
url = {https://dx.doi.org/10.1088/1741-2552/ac9860},
doi = {10.1088/1741-2552/ac9860},
year = {2022},
date = {2022-10-28},
urldate = {2022-10-28},
journal = {Journal of Neural Engineering},
volume = {19},
number = {5},
abstract = {{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},
pubstate = {published},
tppubtype = {article}
}
{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.2021
2.Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article
In: Bernstein Conferen, 2021.
Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021b,
title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p005},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conferen},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
1.Lehmler, Stephan Johann; Saif-ur-Rehman, Muhammad; Glasmachers, Tobias; Iossifidis, Ioannis
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification Artikel
In: 2021.
Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning
@article{lehmler2021deep,
title = {Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://api.semanticscholar.org/CorpusID:245634948},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning},
pubstate = {published},
tppubtype = {article}
}