- 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
Proceedings of the 9th International Conference on Sensor Network
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.
Transforming Seismocardiograms Into Electrocardiograms by Applying Convolutional Autoencoders
2020
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings
45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020) <45, 2020, Barcelona, Spain>
Electrocardiograms constitute the key diagnostic tool for cardiologists. While their diagnostic value is yet unparalleled, electrode placement is prone to errors, and sticky electrodes pose a risk for skin irritations and may detach in long-term measurements. Heart.AI presents a fundamentally new approach, transforming motion-based seismocardiograms into electrocardiograms interpretable by cardiologists. Measurements are conducted simply by placing a sensor on the user’s chest. To generate the transformation model, we trained a convolutional autoencoder with the publicly available CEBS dataset. The transformed ECG strongly correlates with the ground truth (r=.94, p<.01), and important features (number of R-peaks, QRS-complex durations) are modeled realistically (Bland-Altman analyses, p>0.12). On a 5- point Likert scale, 15 cardiologists rated the morphological and rhythmological validity as high (4.63/5 and 4.8/5, respectively). Our electrodeless approach solves crucial problems of ECG measurements while being scalable, accessible and inexpensive. It contributes to telemedicine, especially in low-income and rural regions worldwide.
Vibroarthrography using Convolutional Neural Networks
2020
Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments
ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>
ACM International Conference Proceedings Series (ICPS)
Knees, hip, and other human joints generate noise and vibration while they move. The vibration and sound pattern is characteristic not only for the type of joint but also for the condition. The pattern vary due to abrasion, damage, injury, and other causes. Therefore, the vibration and sound analysis, also known as vibroarthrography (VAG), provides information and possible conclusions about the joint condition, age and health state. The analysis of the pattern is very sophisticated and complex and so approaches of machine learning techniques were applied before. In this paper, we are using convolutional neural networks for the analysis of vibroarthrographic signals and compare the results with already known machine learning techniques.
Wrist-worn Accelerometer based Fall Detection for Embedded Systems and IoT devices using Deep Learning Algorithms
2020
Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments
ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>
ACM International Conference Proceedings Series (ICPS)
With increasing age, elderly persons are falling more often. While a third of people over 65 years are falling once a year, hospitalized people over 80 years are falling multiple times per year. A reliable fall detection is absolutely necessary for a fast help. Therefore, wristworn accelerometer based fall detection systems are developed but the accuracy and precision is not standardized, comparable or sometimes even known. In this paper, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly.
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
Eignung von Convolutional-Auto-Encodern zur automatisierten Fehlererkennung bei Serienbauteilen am Beispiel von Airbag-Generatoren
2019
Rostock, Univ., Master Thesis, 2019