Johannes Hoffmann: Magnetoelectric Sensors for Motion Tracking and Analysis
Pdf-based submission (soon available freely via the MACAU system), 2026
Magnetic fields enable relative (point-to-point) tracking between body-worn devices. This is desirable for clinical gait analysis because it facilitates the wearable assessment of mobility-limiting diseases in patient’s usual environments. Magnetoelectric (ME) cantilever sensors are a novel option for resonant magnetic sensors that may be applicable in this context. This dissertation explores the application of ME sensor-based motion tracking in clinical movement analysis. First, the fundamental performance drivers and feasibility of ME sensors for general-purpose motion tracking in a constrained movement scenario were investigated. This included a system-theoretical and magnetic model of the applied setup with simplified assumptions regarding sensor behavior and the operation of multiple magnetic actuators (coils). Based on the corresponding spatial and temporal considerations, an analytic distance estimation algorithm using triaxial actuators and sensors was developed. The goal was a comprehensive motion tracking system with a network of multiple distributed actuator and sensor nodes. The corresponding real-time signal processing chain was implemented as a modular system that covers aspects such as actuator and sensor signal enhancement, multi-channel access management, spatial estimation, and power distribution. It was co-developed with the required hardware interfaces for sensor and actuator operation. The resulting distance estimates were further enhanced by a two-stage calibration scheme, which was applied at the sensor level (equalizer design) and at the system level (gradient descent optimization). The resulting accuracy in gait trials was less than 1 cm compared to a state-of-the-art optical motion capture system. The tracking algorithm was further improved to enable position and orientation tracking with one actuator and one sensor node, which was validated in a technical trial. The step towards a concrete clinical application was achieved with a feature extraction stage to derive step width, a motor marker for gait stability, from the abstract distance estimates. The approach was to exploit geometric properties of a dual shank-worn ME sensor array during the mid-swing phase of the human gait cycle. This was successfully demonstrated in a pilot study with eight healthy participants. The key findings from these three studies were combined into a short concept study on the requirements of an application-ready tracking system. Overall, the demonstrated estimation performance was suitable for human motion tracking and analysis. The system represents a stepping stone for further approaches of distributed magnetic motion tracking in this regard. The software is hardware-agnostic and can be used flexibly with different sensor types. The approach is especially promising as a complementary tracking system in wearable sensor solutions.
Frederik Kühne: Development of a Real-time, Modular, Multistatic MIMO SONAR System
Pdf-based submission (soon available freely via the MACAU system), 2026
This thesis deals with the development of a modular and multistatic Sound Navigation and Ranging (SONAR) system with the possibility of using orthogonal transmission sequences. In addition, the implementation of the algorithms in a real-time capable overall system is presented and tested in a simulation environment and in measurements. The problem that the measurement results of Multiple Input Single Output (MISO)-SONAR do not meet the expected results is discussed. The lack of orthogonality of the Code Division Multiplex (CDM) sequences used is identified as a problem and alternative forms of orthogonal sequences are investigated against the background of practical use in SONAR systems. An adapted, pseudo-omnidirectional Frequency Division Multiplex (FDM)-MISO-SONAR system is presented and its results are shown simulatively and with measurements. A method for suppressing the resulting grating lobes by sorting the frequency bands is presented and the utilisation of overlapping frequency bands is shown to extend the effectively usable bandwidth.
The second part deals with the construction of a network of distributed SONAR nodes. For this purpose, the creation of monostatic and bistatic individual maps is first presented using a simple, downstream equalisation. The fusion of the individual SONAR maps and their weighting via the Signal to Noise Ratio (SNR) is shown. The overall system is tested simulatively and with measurements and an increase in the Signal to Noise and Clutter Ratio (SNCR) for certain scenarios is shown. The combination of distributed systems with pseudo-omnidirectional Multiple Input Multiple Output (MIMO)-SONAR is also presented.
The third part shows the implementation of the developed algorithms with a focus on realtime capability. The modular, flexible software architecture is presented and the result is tested using two exemplary scenarios. A complex, distributed SONAR system can be operated flexibly and in real time with the developed system on a single standard computer and adapted during runtime.
Nawar Habboush: Solving the Inverse Problem for Localizing the Biomagnetic Activity in the Heart
Pdf-based submission (available freely via the MACAU system), 2024
This thesis aims to develop a comprehensive solution for both forward and inverse problems in modelling the human heart, with a specific focus on accurately analysing MCG and ECG datasets. The research methodology involves a meticulous process of data recording, MRI processing, and constructing a multi-regional model that segments different tissues based on their relevant characteristics, ultimately providing a solution for the forward problem. The proposed approach employs the use of Kalman filter and state-space models, followed by the GARCH model to solve the inverse problem, resulting in improved accuracy of data analysis and source localization.
This thesis marks the first attempt to apply the Kalman filtering methodology to analyse MCG data, drawing from extensive experience gained in brain research, particularly for EEG and MEG datasets. The proposed approach has been tested and validated using both simulated and real MCG and ECG datasets from individuals, demonstrating its efficacy in analysing heart activity data and its immense potential for clinical applications. The significance of this research lies in its potential implications for the diagnosis and treatment of various heart conditions. The developed methodology can precisely localize sources of heart activity, aiding in diagnosis and intervention planning, such as ablation or pacemaker implantation. The non-invasive method of activity localization using MCG and ECG datasets, as compared to the invasive method of using a catheter, opens up new avenues for the diagnosis and treatment of heart conditions.
While simpler inverse problem methods that do not require high computational power can be used to find the source activity of both MCG SQUID and ECG electrodes datasets, this thesis also aims to provide data analysis for sensors with a lower signal to noise ratio like the Magnetoelectric Sensors being developed in Kiel. The usage of such sensors is cheaper in terms of initial device costs and running costs.
This interdisciplinary research presents a novel methodology for analysing MCG and ECG datasets that has the potential to revolutionize the field of medical science, specifically the diagnosis of heart conditions. The immense potential of this research highlights the significant contributions that interdisciplinary research with engineering can make towards advancing medical science.
Christin Bald: Echtzeitlokalisierung magnetoelektrischer Sensoren
Pdf-based submission (available freely via the MACAU system), 2024
The measurement of magnetic fields is becoming increasingly important in medicine. The signals measured by magnetic field sensors outside the body can be used to infer the processes inside the body. This is done by solving a so-called inverse problem, which needs the positions and orientations of the measuring sensors besides the signals. The positions and orientations should be determined continiously during measurement, since they are not necessarily fixed during a magnetic measurement. This is done by a magnetic localization. This work presents a signal processing chain for magnetic localization in real time. This includes preprocessing steps for extraction of important information from the sensor data, the estimation of the position and orientation of the sensor(array)s and postprocessing of the estimated data. The proposed signal processing chain will be first described theoretically and afterwards evaluated by means of simulations and measurements. The signal processing chain is suitable for all kinds of magnetic sensors in principle. This work focusses on magnetoelectric sensors. Commercially available fluxgate magnetometers have been used for comparison purposes. Different parameters and distortions are investigated, that could have an influence on the localization accuracy. For example, the accuracy at different signal-to-noise ratios will be examined as well as the robustness in the presence of different errors in the forward model, like deviations from the sensor modell or the coil positions and orientations. The measurements are executed with a single sensor in 2D as well as a single sensor and a 3D sensor in 3D. In total, a higher localization accuracy can be achieved with the fluxgate magnetometers in comparison to the magnetoelectric sensors. Nevertheless it can be shown, that the magnetoelectric sensors are suitable for magnetic localization. Besides handing over the position and orientation information for the solution of different kinds of inverse problems, the localization can be used as a stand-alone application. This will be investigated in this work by localizing an ultrasound head. The localization accuracy is not impaired by the ultrasonic device and thus by the potential source of interference.
Arthur Wolf: Entwurf und Evaluierung von Algorithmen für ein Innenraumkommunikationssystem
Shaker-Verlag, 2023
The communication inside a car is often difficult due to the unfavorable seating position and the high background noise level. An in-car communication system (ICC) can improve the communication between passengers while driving. For this purpose, the voice is recorded with a close to the speaker mouth placed microphone and, after appropriate signal processing, it is played back via close to the listener positioned loudspeakers. The ICC system operates in a closed electro-acoustic loop. Therefore, the maximum system amplification is limited by the feedback of the playback signal. Because the system gain required for sufficient support is often in the range of the maximum gain, additional measures must be taken to suppress feedback. In addition to the speech and the feedback signals, the microphones also pick up the driving noises within the vehicle. If the noisy microphone signal is only amplified, the recorded interference will have a negative effect on the speech quality. These signal components should be reduced by suitable noise suppression to improve the ICC output signal.
For the passengers, the perceived system quality depends not only on the amplification level but also on the delay introduced by the ICC system between the direct sound and the system reproduction. This delay should be as low as possible and typically not exceed 15 ms. The acoustic localization of the speaker and the speech intelligibility of the playback is disturbed by a long delay. Due to psychoacoustic effects, the permissible delay depends heavily on the system gain and the arrangement of the loudspeakers in relation to the passengers.
In this work, the signal processing for an ICC system, which works robustly in a vehicle under real conditions, is presented. The focus is on runtime-optimized and computationally efficient algorithms for noise and feedback suppression. The delay at the listener’s ear is reduced to only 10 ms. With the feedback suppression measures described here, the ICC system can also be operated around maximum system gain without instabilities. The implementation of algorithms as real-time digital audio processing and the buildup of a real time demonstrator vehicle made it possible to evaluate the improvement in speech intelligibility achieved in the vehicle environment. In addition to the improvement in speech transmission recorded by measurements, the speech intelligibility and speech quality were confirmed in experiments by test subjects. The results of this evaluation show that with an active ICC system and the signal processing and measures presented here, the passengers on the rear seats (worst listening position) can hear and understand the driver just as well, as the front passenger (best listening position), even at high speeds.
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In March 2026, the DSS Chair attended the annual DAGA conference in Dresden. Thanks to the support of the GaS-Club, the student Aylin Kösker was given the opportunity to accompany the chair and participate in the conference from March 23rd to March 26th. As part of the daily poster sessions, she presented the results of her bachelor’s thesis “Machine Learning for the Analysis of Hydrographic Data to Assess the Waterside Accessibility of Port Waters” in the field of Underwater Acoustics. The thesis forms an important basis for an ongoing university research project on the acoustic analysis of sediment properties in harbor areas. The poster session enabled valuable discussions with researchers and conference participants from related research fields.