- Vita
- Position description
- Publications
Dimitri Kraft studied Computer Engineering (B. Sc.) at the Hochschule für Technik und Wirtschaft (University of Applied Sciences for Engineering and Economics) in Berlin and received his M. Sc. in Visual Computing at the University of Rostock in 2019.
In the same year, he started his career as a research assistant at the Visual Computing Research and Innovation Center (VCRIC), which is a joint institution of the Fraunhofer-Gesellschaft and the University of Rostock.
Dimitri Kraft develops new methods and technologies to classify, transform and segment time series data. The focus of his research is on developing new methods to assess the health status of human joints and apply deep learning algorithms to classify vibrations and sounds of human joints (vibroarthrography).
Enhancing Vibroarthrography by using Sensor Fusion
2020
VISIGRAPP 2020. Proceedings
International Joint Conference on Computer Vision and Computer Graphics Theory and Applications (VISIGRAPP) <15, 2020, Valetta, Malta>
Natural and artificial joints of a human body are emitting vibration and sound during the movement. The sound and vibration pattern of a joint is characteristic and changes due to damage, uneven tread wear, injuries, or other influences. Hence, the vibration and sound analysis enables an estimation of the joint condition. This kind of analysis, vibroarthrography (VAG), allows the analysis of diseases like arthritis or osteoporosis and might determine trauma, inflammation, or misalignment. The classification of the vibration and sound data is very challenging and needs a comprehensive annotated data base. Current existing data bases are very limited and insufficient for deep learning or artificial intelligent approaches. In this paper, we describe a new concept of the design of a vibroarthrography system using a sensor network. We discuss the possible improvements and we give an outlook for the future work and application fields of VAG.
A Survey on Vibration and Sound Analysis for Disease Detection of Knee and Hip Joints
2019
iWOAR 2019
International Workshop on Sensor-based Activity Recognition (iWOAR) <6, 2019, Rostock, Germany>
ACM International Conference Proceedings Series
The knee is the largest joint in the human body. Unfortunately, some hips or knee joints suffer on inflammation, misalignment, degeneration, trauma as well as diseases like arthritis or osteoporosis. Modern medicine can measure the joint condition or, if the joint is worn out, even exchange the joint with an implant. Endoprosthetic implants are artificial devices that replaces a weak body part such as osteoarthritic knee or hip joints. The lifespan of joint endoprostheses are also limited and depend on several factors, and it varies for each patient. In most cases total knee or hip endoprostheses need to be replaced after approximately 15 to 20 years, but some implants need an exchange after a few years due to several causes. Current methods to examine the condition of joint endoprostheses and natural joints are X-ray, Computed tomography (CT) and Magnetic Resonance Imaging (MRI). In rare cases implant integrated sensors were used. The usage of these methods and the analysis of the assessed data require medical and data experts. However, a vague estimation of the joint condition can also be performed by external vibration and sound analysis of the endoprosthesis and natural joint during movements. This paper describes several approaches of external vibration and sound analysis as a survey