Im Teilprojekt B04 werden innovatiive Verfahren zur Visualisierung von Bewegungsabläufen, die mit verschiedenen empathokinästhetischen Sensoren erfasst und/oder für die eine biomechanische Simulation durchgeführt wurde, erforscht. Der Fokus liegt dabei auf Visualisierungen und der Extraktion von Merkmalen, die bei der medizinischen Interpretation und Diagnose unterstützen.
The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
Egger, B., Sutherland, S., Medin, S.C., & Tenenbaum, J. (2021). Identity-Expression Ambiguity in 3D Morphable Face Models. In 2021 16TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2021). NEW YORK: IEEE.
Shetty, K., Birkhold, A., Strobel, N., Jaganathan, S., Kowarschik, M., Maier, A., & Egger, B. (2021). Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 290-295). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Smith, W.A.P., Seck, A., Dee, H., Tiddeman, B., Tenenbaum, J.B., & Egger, B. (2020). A morphable face Albedo model. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 5010-5019). Virtual, Online, USA: IEEE Computer Society.
I study how humans and machines can model and perceive 3D shapes and faces.
Research Projects
Current projects
Visualisierung von Bewegungsabläufen basierend auf einem biomechanischen Modell
(Third Party Funds Group – Sub project)
Modellbildung und Zustandsbestimmung (EmpkinS)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
Im Teilprojekt B04 werden innovatiive Verfahren zur Visualisierung von Bewegungsabläufen, die mit verschiedenen empathokinästhetischen Sensoren erfasst und/oder für die eine biomechanische Simulation durchgeführt wurde, erforscht. Der Fokus liegt dabei auf Visualisierungen und der Extraktion von Merkmalen, die bei der medizinischen Interpretation und Diagnose unterstützen.
Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
Recent publications
2022
2021
2020
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