Theses

Bachelor’s Theses

Candidate: 

Simon Veittes

Supervisor: 

Dr. Stephan Jonas,
Dr. Jó Ágila Bitsch,
Marko Jovanović
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino,
Univ.-Prof. Dr.-Ing. Klaus Wehrle

From --- to 25.08.2017





The education of motor skills is nowadays supported by wearable devices in so called serious games. This innovation is also present in the medical and dentistry education. A first interactive game for the alginate mixing process was created by Hannig et al. in 2011 called Skills-O-Mat. This game is outdated and reengineered to a new technology and a new interactive device. Therefore, a website is created which provides the functionality of the gameplay. The input device is changed to a Myo armband. The connection between the wristband and the JavaScript website environment is achieved through the Myo Connect software provided by Thalmic. Also, the evaluation algorithm is reimplemented to fit the new input data. Therefore, a Multilayer-Perceptron is implemented and trained with the data of a professional dentist. This neural network recognizes the different gestures done during the mixing steps. With this new version of the game, it is published to a greater audience and is more portable. The scoring scheme reflects the quality of the mixed alginate and a scoring about 50% cannot be tricked. Skills-O-Mat is reimplemented as web based serious game sensor-assisted by the Myo armband. The quality of the mixed alginate is measured with the help of a neural network which detects the different gestures during the mixing process. Additionally, a real-time feedback to the detected gestures is given. Whether the new evaluation leads to a better training effect is not reviewed yet, but a training effect is detected in a small volunteer evaluation.

Candidate: 

Tarek Chebbi

Supervisor: 

Dr. Stephan Jonas,
Dr. Jó Ágila Bitsch,
Marko Jovanović
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino,
Univ.-Prof. Dr.-Ing. Klaus Wehrle

From --- to 09/2017





The Myo by Thalmic Inc. is an electronic sensor device worn on the forearm, which is able to detect arm movements and hand gestures. The detection is based on evaluating sensor data from an integrated inertial measurement unit (IMU) and eight surface electromyography (sEMG) sensors located along the perimeter of the armband. The detection quality of the arm movements and hand gestures is highly dependent on the quality of the data delivered by the device’s sensors and the calibration method implemented by Thalmic via their software. This existing calibration is solely based on the gestures developed by the company itself, while its specifics and implementation remain unknown and are therefore not alterable. It improves the detection quality through the execution of an initial gesture and, optionally, by learning from the user’s multiple executions of the existing gestures. For these reasons the calibration is not suited for the development and improvement of gestures, which are not developed by Thalmic. This thesis proposes a method that aims to improve the detection quality not only of the existing gestures but newly developed gestures as well, by preprocessing the data acquired via a direct Bluetooth connection to the device, effectively bypassing most of Thalmic’s calibration process. The primary goal of this thesis is the calibration of user-parameters, such as device position, rotation, and immutable influences such as skin moisture and body fat for the sEMG or axis misalignment and scaling errors of the IMU. The acquired data is tested for the existence, magnitude and effects of these errors, to check for already implemented software and hardware calibration methods, that could have been applied before the data is sent and thereby determine the errors and appropriate methods for further calibration. Additionally, an algorithm requiring the user's interaction is developed, attempting to resolve errors due to changeable influences and thus further increase the quality of the sensor data as well as the gesture detection directly. However, the algorithm should require as little user interaction as possible, since user variance is a considerable source for errors. The algorithm requires the user to provide an approximate forward vector, which is used in conjunction with the gravity vector provided by the pre-calibrated accelerometer, to rotate the IMU coordinate system into a user coordinate system. Furthermore, the unique layout of the Myo, with its eight sEMG sensors evenly distributed around the user's arm, requires a rotation of the armband around the arm as part of the calibration procedure to achieve the best possible quality of raw EMG data. To determine whether the rotation-correction can be automatically applied by a mathematical model, the results of such a rotation are compared with a rotation applied by the user. The final calibration method is evaluated on a set of self-captured gestures. Additionally, a publicly available dataset is used, containing a total of 154 captures composed of fourteen official basketball referee gestures, including five number indicating gestures, which are performed by eleven subjects. The method is evaluated with a slightly modified version of a solution (Laukamp, 2015) for gesture recognition in hand hygiene, which compares three different approaches to gesture recognition: k-Nearest-Neighbors, Support Vector Machines and an approach which combines Artificial Neural Networks with Hidden Markov Models.

Candidate: 

Johannes Seiffarth

Supervisor: 

Dr. Stephan Jonas,
Marko Jovanović,
Dr. Jó Ágila Bitsch
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino,
Univ.-Prof. Dr.-Ing. Klaus Wehrle

From --- to 08/2017





Proprioceptive Neuromuscular Facilitation (PNF) is a special concept of treatment in the field of physiotherapy. The stimulation of sensory receptors and muscles are combined with supporting a specific patient’s movement execution by the therapist. It is often used to treat neurological, traumatic as well as orthopedic disorders in rehabilitation processes. Due to the high complexity of PNF movements, teaching the therapy method to students is time consuming and requires adequate and proper training. However, only a limited amount of supervised training time is usually available in current edu- cational settings. Still, individual feedback on the movement execution is important for trainees to improve their PNF skills. Additionally, learning or improving move- ment execution from descriptions in books is difficult. In recent years, interactive eLearning approaches have become more popular because they motivate trainees and can be integrated into existing scholar environments. Existing systems for movement evaluation and analysis are large, complex and expensive setups using multiple cam- eras or body sensors and are, therefore, not suitable for teaching environments. As an alternative, smart wearable devices are inexpensive, do not hinder the student and can be connected to today’s computationally powerful smart phones. Yet, these wearables are limited in their sensory capabilities and the sensor data is error-prone. Nevertheless, they allow for the construction of a lightweight, simple and inexpensive system that is required for an eLearning application. This thesis proposes a system using two wearable Thalmic Myo armbands for rec- ognizing errors in the movement execution of a given PNF movement and providing constructive feedback to the executing student. The thesis deals with three main tasks: Firstly, it aims at finding well performing feature sets based on the recorded data containing important information about the movement execution. Secondly, a set of recorded“gold standard”PNF movement executions will be used to train Hid- den Markov Models especially designed for modeling movements and dealing with variations in both sensory measurements as well as variations arising from different executing therapists and patients. Thirdly, the trained model will be used to provide feedback in form of classification, scoring and identification of error sources. The evaluation of the developed methods shows promising results in modeling capabilities of PNF and basketball referee movements. The scoring allows to easily distinguish between correct and erroneous movement executions. Furthermore, the identifica- tion of error sources allows to detect time durations of errors and the features that show abnormalities. The developed methods show promising results for automated feedback generation and still have a large potential of further development. Therefore, this work is a major step towards the development of an eLearning application in physiotherapy.

Candidate: 

Bernd Hankammer

Supervisor: 

Jó Ágila Bitsch,
Dr. Stephan Jonas,
Dr. Ekaterina Kutafina
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 02/2017





Electroencephalography (EEG) is one method to investigate different mental health problems, like dementia, depression, schizophrenia or post-traumatic stress disorder. Based on studies and the rapidly expanding population, the numbers of people with these diseases are expected to increase even further. Clinical EEG devices are stationary, large in size, complex and expensive. Over the past years many commercial mobile EEG devices have been developed. To perform a complete mobile EEG measurement, corresponding apps on mobile devices, such as tablets or smartphones, need to be developed. An app for conduction of experiments was already developed in previous work. In this work BrainLabP&V, which is a mobile EEG visualization and processing framework, is presented. By loading a previous recorded EEG measurement, the proposed system can visualize and process the data. Here, a visual programming approach is used to ease the operability, while increasing usability and allows the system to adapt to changing user requirements. This makes BrainLabP&V another important step towards completely mobile brain research.

Candidate: 

Julia Barth

Supervisor: 

Dr. Stephan Jonas,
Jó Ágila Bitsch
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 11/2016





The demographic distribution has dramatically changed over the last decades, re- sulting in an older population. This creates a situation where more caregivers – including informal caregivers – are needed. Informal caregivers often face higher stress due to their personal relation to the caretaker and the fact that they are not trained in clinical tasks. This might lead to health problems of the caregivers them- selves, resulting from false posture or physical fatigue. To overcome these problems, a smart shirt to detect false posture and physical fatigue has been developed. This smart shirt requires an infrastructure to record and to analyze the data. The aim of this thesis is to develop this infrastructure to record data from the smart shirt and analyze it regarding posture and stress. This infrastructure is realized as an Android application, which connects to the smart shirt, records, stores, displays, and analyzes the data. The analysis methods are focused on posture and stress to face the main health challenges of informal caregivers. The application combines the recording and analysis of the data regarding posture and stress. With this combination of recording and analysis the application provides a low cost and easily available solution for informal caregivers. This thesis contributes design, development, and evaluation of the corresponding application. For the latter, we developed and conducted a user study in which the participants performed various tasks. This study was used to identify whether the data from the smart shirt is significant to posture and stress. Ultimately, the application enables informal caregivers to monitor their health regarding posture and stress in an easily accessible and low cost fashion. This contributes to protecting the health of informal caregivers during their tasks caring for someone else.

Candidate: 

Sascha Welten

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.-Ing. Ulrik Schröder,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 09/2016





The media frequently report about hospital-acquired infections and the devastating effects. Since the hospital staff are in direct contact with the patients, the hands of the caregivers are the ideal germ carriers. The transmission of germs from patient to patient can be prevented by a proper hand washing or hand disinfection procedure. However, the workers do not often follow the hygiene guidelines due to a lack of time or convenience. The preliminary work of Laukamp et al. carried out a study and tried to figure out the effectiveness of hand hygiene training of prospective nurses. The result was that the trainees had problems in the practice of the described gestures. Thus, these deficits could be an additional risk of germ transmission. This work is a proposal to solve this problem of lacking practical experience. The task is to develop a mobile application which offers the trainees an opportunity to practice the individual gestures. Based on the work of Laukamp et al., we use concepts of gamification to keep the motivation of the users on a high level constantly. So, our application motivates the user to perform hand washing gestures and is able to assess the performed gestures. Additionally, the user is rewarded for a good performance. This work tries to answer two research questions: (1) Is the concept of gamification a good approach to motivate the user, and (2) which implemented gamification approach is the most promising in the described setup. To answer the questions, a user study was carried out to evaluate the usability and the aspects of gamification. The findings of this study are used to formulate tasks for the future work.

Candidate: 

Daniela Albiez

Supervisor: 

Jó Ágila Bitsch,
Dr. Stephan Jonas,
Dr. Ekaterina Kutafina
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 07/2016





Medical brain examination is crucial for detection and evaluation of disorders like epilepsy. However, complex hardware and technical set-ups are often required in the traditional way. Mobile and effortless healthcare can provide improvements regarding accessibility and costs especially for places with weak infrastructure. Fur- thermore, it creates space for novel scientific research. In this work, BrainLab, which is an mobile EEG presentation framework for brain research, is presented. Using a commercial mobile EEG, the proposed system al- lows to conduct ERP experiments by just using a mobile device like a tablet or smartphone and without sophisticated equipment or infrastructure. Several approaches to examine the viability of our framework for conducting EEG experiments have been exploited and shown promising results. Thus, BrainLab is a fine step toward fully mobile brain research.

Candidate: 

Ina Fink

Supervisor: 

Dr. Stephan Jonas,
Jó Ágila Bitsch
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From 10/2015 to 05/2016





Medical brain examination is crucial for detection and evaluation of disorders like epilepsy. However, complex hardware and technical set-ups are often required in the traditional way. Mobile and effortless healthcare can provide improvements regarding accessibility and costs especially for places with weak infrastructure. Fur- thermore, it creates space for novel scientific research. In this work, BrainLab, which is an mobile EEG presentation framework for brain research, is presented. Using a commercial mobile EEG, the proposed system al- lows to conduct ERP experiments by just using a mobile device like a tablet or smartphone and without sophisticated equipment or infrastructure. Several approaches to examine the viability of our framework for conducting EEG experiments have been exploited and shown promising results. Thus, BrainLab is a fine step toward fully mobile brain research.

Candidate: 

Alexander Lipski

Supervisor: 

Jó Ágila Bitsch,
Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 04/2016





Recent progress in computer vision produced methods that allow to measure human heart rate only using a usual webcam or smartphone camera. This procedures introduce potential advancements in the field of medicine. Current methods for quick heart rate estimations are either overcomplicated for a quick estimation or hygienically questionable. This thesis explores the possibilities of using a computer vision approach in order to achieve a quick and contactless estimation of a patients heart rate using Google Glass. The heart rate is computed by measuring small changes in the skin color, which occur with every heart beat due to differences in the blood volume in the vessels. The method shows that the analysis of the green components is sufficient to get a reasonable estimation. The implementation of the method has shown deficits in hardware and software if the method is to be run in real time on the Google Glass. Since the Glass needs a continuous face detection because of strong movement of the region of interest, the creation of faster methods for face detection or a strong improvement in computational power is necessary if this method is to be run in real time. Possible solutions for creating such applications that run not in real time are finally presented.

Candidate: 

Alexander Brenner

Supervisor: 

Dr. Stephan Jonas,
Jó Ágila Bitsch
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino,
Univ.-Prof. Dr.-Ing. Klaus Wehrle

From --- to ---





The electroencephalogram (EEG), a signal measurement of the electrical activity of the brain, is one of the most common sources of information used to study brain function and neurological disorders. Mobile EEG systems ease long term monitoring and can provide improvements regarding accessibility and costs, especially for places with weak infrastructure. However, it requires an expert to analyze EEG recordings, in order to detect abnormal activities that occur in a wide variety of morphologies and can share similarities to waves that are part of normal EEG or to artifacts. Due to the large amount of data generated by EEG monitoring systems a human visual inspection is time-consuming and inefficient. Nevertheless, EEG is an important tool that is used to monitor side effects associated with medical drugs as for instance clozapine. Clozapine is an atypical antipsychotic drug that has been shown to be effective in treatment-resistant schizophrenia and patients with intolerance to other neuroleptic drugs. One of the most prevalent side effects are seizures. Additionally various types of EEG abnormality are observable in many patients. Generalized slowing has been reported as the most frequent finding, followed by interictal epileptiform discharges such as spike and sharp wave activities that are typically observed in epileptic patients between seizures. While some studies on clozapine treatment mention epileptiform EEG abnormality as an indicator for seizures, others report that seizures are not necessarily predictable by previous EEG changes. Hence, long term EEG investigations are important in analyzing the effect of clozapine dose on EEG and the relationship between EEG alterations and seizures. This thesis explores automated methods for the detection of abnormalities in order to support neurologists with the analysis of EEG recordings. We organize the various forms of EEG abnormalities in clozapine treated patients to investigate appropriate detection algorithms. Thus, we examine methods for the detection of slow activity as well as approaches for the detection of epileptiform discharges. To our best knowledge, currently no dataset with manually marked individual spikes and sharp wave discharges is publicly available. Therefore in the thesis a subset of the Temple University EEG Corpus that provide recordings categorized into normal EEGs and abnormal EEGs will be used to examine the developed pipelines.

Master’s Theses

Candidate: 

Frederic Klein

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Prof. Dr. rer. nat. Alexander Voß

From --- to 12/2016



In recent years gamification has become a part in many areas of our daily routine. In regard to our personal life, companies like Amazon or Runtastic can base their gamification approach on publicly sharing personal achievements and statistics to improve user commitment. In contrast, gamification concerning our work life has to satisfy much higher privacy demands. Since comparison is a key component for gamification, privacy protecting computations of system wide statistical values (for example minimum and maximum) are needed. The solution comes in the form of secure multi-party computation (SMPC), a subfield of cryptography. Existing frameworks for SMPC utilize the Internet Protocol, though access to the Internet or even a local area network (LAN) cannot be provided in all environments. Facilities with sensible measuring systems, e.g. medical devices in hospitals, often avoid Wi-Fi to reduce the risk of electromagnetic interference. To be able to utilize SMPC in environments with Wi-Fi restrictions, this thesis studies the characteristics of mobile ad hoc networks (MANET) and proposes the design of a SMPC framework for MANET, especially based on Bluetooth technology, and the implementation as a C library. Since MANETs have a high probability for network partition, a centralized architecture for the computation and data preservation is unfavorable. Therefor a blockchain based distributed database is implemented in the framework. Typical problems of distributed systems are addressed with the implementation of algorithms for clock synchronization and coordinator election as well as protocols for the detection of computation partners and data distribution. Since the framework aims to provide distributed computations of comparable values, protocols for secure addition and secure comparison are implemented, enabling the computation of minimum, maximum and average. Devices of diverse computational power will be used to verify the applicability for wearables and Internet of Things (IoT) grade devices. Also field-tests with a smart phone ad hoc network (SPAN)(20-50 nodes) will be conducted to evaluated real life use cases. In contrast, the security of the framework and attack scenarios will be discussed. In summary, this thesis proposes a framework for SMPC for decentralized, distributed systems.

Candidate: 

Andreas Burgdorf

Supervisor: 

Dr. Stephan Jonas,
Jó Ágila Bitsch
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 09/2016





In Germany, 25 percent of the adults suffer from sleep disorders, which can have dangerous impacts like causing accidents or are followed by further disorders. Many examinations that help to detect sleep disorders can only be done in sleep laboratories, or need expansive hardware for usage at home. A possibility to perform a cheaper and long-time monitoring of patients are smart wearable devices (wearables) like smartwatches, which are equipped with sensors for a minimal monitoring. This thesis aims at the development of a mobile sleep laboratory. Intended is a platform based on Android, which collects and combines data from different sensors of different devices. These sensors shall substitute the devices that are used in a sleep laboratory as good as possible. To design the sleep laboratory as modular as possible, the different components for monitoring and presenting data shall be exchangeable. This allows the platform to be used for later research.

Candidate: 

Zafer Shishkov

Supervisor: 

Dr. Stephan Jonas,
Jó Ágila Bitsch
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 07/2016





With the recent changes in the German Civil Code (Bu ̈rgerliches Gesetzbuch (BGB)) patients’ will information is prioritized above all. Health care professionals must al- ways follow it when taking decisions regarding patients’ treatment. Advance Care Planning (ACP) is a prominent approach for patients to state their preferences in terms of accepted (or denied) future treatments. The will information is prepared together with health care professionals and expressed in a written form, e.g., organ donation cards, living wills or health care proxies. Patients may also state their representatives, who may make health-related decisions on patients’ behalf, in cases when the patients are not able to communicate or are legally incompetent to take decisions. Till now clinicians performed a time consuming and error-prone will infor- mation acquisition procedure, which typically involves patients’ relatives or persons empowered by the patients. The procedure is not applicable in emergency or life threatening situations, when the timely acquisition of patients’ will information is crucial for the decision making process. Moreover, the provided information is often open to interpretations and not objective. A new approach to the problem of secure storage and delivery of patients’ will in- formation can be offered by Electronic Health Record (EHR) systems. They create, process and store digital health data, such as patients’ will information or history of patients’ diagnoses. This new approach allows timely delivery of patients’ will information on demand in a secure manner. We examined such EHR systems de- ployed in Europe. As far as we know none of the systems offers mobile applications for access to or management of patients’ will information. This master thesis examines an alternative EHR system, which solves the problem of secure storage and delivery of patients’ will information by means of innovative mobile applications and a storage and processing module. This system delivers valid and up-to-date patients’ will information in emergency or life threatening situations. It guarantees the privacy of patients and health care professionals by pseudonymizing their personal information. Moreover, the confidentiality and integrity of the will information is assured during its entire life cycle through deployment of state-of-the- art cryptographical algorithms and protocols. Patients’ health data is stored in a secure data vault. The access to and the management of the health information is realised through two mobile Android applications. Patients’ will information may also be validated. The deployed security controls ensure patients’ data sovereignty. Furthermore, the EHR system is compliant with the current German data protection and information security legislation. Considering the time-critical nature of the application scenarios the performance of system’s information delivery and validation functions are tested in terms of execu- tion time, processor load, memory and battery usage. The capability of the system to continue providing will information by peak loads is also examined. Moreover, the fulfillment of system’s design specifications is evaluated. The test results confirm system’s legal compliance and demonstrate the significant performance potential of the system under typical or peak loads. Means to improve system’s user interface and to extend the provided functionality are also discussed.

Candidate: 

Marko Jovanović

Supervisor: 

Dr. Stephan Jonas,
Christoph Greven
Examiner: 

Univ.-Prof. Dr.-Ing. Ulrik Schroeder,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 06/2016





Collaborative scientific writing is gaining increasing importance as the number of published scientific paper rises as well as the number researchers collaborating on research projects. Tool support for collaboration on scientific projects is still in its infancy, and students, researchers and professors are left with unsatisfactory software support for collaboration on research projects that leaves room for improvement. The lack of adequate software support motivates the question for possible ways of improving the current state of the art. In this thesis, a user study researches the habits and needs of researchers performing collaborative scientific writing at two universities. From the results of the study, a set of requirements is gathered for a Social Collaborative Research Platform. From the deducted requirements, a design approach and subsequent implementation of the novel tool starTeX is conducted, aiming to improve the current state of the art of tool support for collaborative writing. The design approach is based on research findings of Computer Supported Cooperative Work and takes the research theory of collaborative writing and three core aspects of systems supporting collaborative writing into account: technical, organizational and social. Especially the social component of the software aims to further increase the capability spectrum of current software and acknowledges the social aspect of collaborative writing process. Specifically, an approach of integrating the novel tool with a recent business-oriented social network software is presented. The presented software undergoes evaluation regarding the aspects of collaborative writing by a group of students and researchers. The results of the user survey have shown promising results and motivate further development of the proposed platform.

Candidate: 

Niklas Kobelev

Supervisor: 

Dr. Stephan Jonas,
Dr. Ekaterina Kutafina
Examiner: 

Dr. Joel Karel,
Dr. ir. Kurt Driessens

From --- to 06/2016




It is common that people undergo a regular medical check-up. But very few of us examine their mental health, whereas it plays one of the main roles in our daily lives. This work will be a part of a bigger project with the purpose to develop a screening system for identifying mental disorders and neurological problems. The main goal of this thesis is to develop a modular system which explores a combination of signal processing and machine learning techniques to analyse electroencephalography (EEG) data. The system is evaluated by a particular task of recognition of four emotions: fear, disgust, joy and excitement. Yet, the proposed platform is not bound to that specific problem and can be used in a wide range of applications. There exist several results on emotion recognition by means of the same EEG device. However current project uses a mobile tool to perform the experiments and a different kind of stimuli to evoke the emotions. An Emotiv Epoc headset with 16 electrodes is used to measure the EEG data from the test subjects who will be presented with the visual stimuli from the IASP database. Two different experimenting platforms for stimuli presentation and data acquisition will be used and evaluated: a stationary PC and a fully mobile tablet-based system recently developed in the institute. The results of the current work show that the mobile tool is applicable for the aimed task. The initial recognition rate of 30% for four classes could be increased up to 44% by performed optimizations and parameter adjustments. The results are above the chance and promising, but the design of the experiments should be reconsidered and a greater dataset should be built.

Candidate: 

Ahmed Bani Yassien

Supervisor: 

Thiru Kanagasabapathi PhD,
Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Dipl.-Math. Klaus Kabino,
Thiru Kanagasabapathi PhD

From --- to 12/2015





Non-invasive visualization of structures underneath the skin and organ surface in open surgeries and interventional procedures may help in surgery planning and navigation. In surgeries, visualization of concealed critical structures before dissection prevents surgical complications and results in better clinical outcome. During my thesis, I will be working on a part of running project at Philips research to develop an innovative hybrid operating room. My contribution is to determine the applicability of multispectral imaging in enhancing visualization of underlying structure non-invasively based on their optical properties. These structures will be utilized in potential clinical applications at present patient’s motion compensation during surgeries is considered as one of these applications. In our approach multispectral camera with filters of specific wavelength in infrared range will be used and the detected spectral signature of subcutaneous structures. Experiments will be carried out on volunteer subjects to build spectral images database for regions of interest (Arm, forearm, hand, lowerback and upperback) Then collected spectral images will be processed toward extracting the most relevant information by developing new image processing algorithms. With appropriate visualization technique and image processing algorithms, the information may be used for planning navigation in critical care interventional surgeries.

Candidate: 

David Laukamp

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.-Ing. Ulrik Schroeder,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to 12/2015





In our time, mobile electronic devices represent one of the areas with the most rapid develop- ment. Smart phones are ubiquitous and offer a level of computational power to the consumer that has surpassed those of super computers of past decades. Complementary, there is a growing market of wearable devices that interact with the user’s smart phone providing various func- tionality and data obtained from integrated sensors. The availability of these cheap and novel devices motivates the question for professional use cases. This work aims to assess the potential of mobile wearable devices in a medical eLearning scenario. We lay out the foundations for an approach that uses the Thalmic Myo to improve hand hygiene training for medical profes- sionals. This could decrease training costs as eLearning applications offer the possibility for unsupervised training and could improve long-term hand hygiene proficiency. To explore the technical feasibility, a machine learning framework is implemented to judge the execution of manual gestures, we focus on those belonging to the internationally applied WHO hand hygiene procedures. In the implemented framework, we compare various machine learning algorithms and data processing routines to maximize gesture recognition accuracy. Additionally, we present a study in which nursing students perform a hand disinfection wearing the Myo armbands to analyze the acceptance of and gather design requirements for a wearable-based eLearning ap- proach to hand hygiene training. This experiment also serves as an evaluation of the current state of hand hygiene quality, realized by measuring the hand cleaning success using fluorescent photographs. Taking into account both cleaning success and gesture recognition reliability, we present an outlook on the further development and other possible areas of application.

Candidate: 

Halim I Baqapuri

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino,
Dr.-Ing. Berno Misgeld

From --- to 12/2015





There is a growing need for caregivers in Germany, professional and non-professional, as the population is gradually growing older due to healthcare improvements. Informal caregivers face many challenges, from insecurity due to improper training for the performed tasks and lack of funding, to overestimating themselves physically and psychologically. The mHealth division at the Department of Medical Informatics, Uniklinik RWTH Aachen is currently conducting research to unobtrusively monitor physical characteristics of caregivers in a clinical as well as a private setting. The goal is to warn the caregivers if they are a performing a task incorrectly and/or if they are over exerting themselves. This thesis will support the on-going research by creating a low-cost wearable device for caregiver support. A prototype electronic system to measure various physical indications of fatigue and tiredness in caregivers will be designed and implemented using hardware readily available on the market. Need for a new system arises as already established devices such as the ‘SenseWear Armband’ are extremely costly, because they have very specialized sensors to measure physical activity. Cheaper versions of wearable devices that measure physical activity focus mainly on exercise and activities such as running/jogging to measure energy expended by the wearer. The system proposed in this project will be a wearable device, powered by an ‘Arduino Lilypad’ board. The device will be housed in a sports vest, designed to be worn under everyday clothing. The device will record signals from various accelerometers and an audio sensor, which will be used to measure the breathing rate, heart rate and movement of the caregiver. These signals will then be collected and communicated wirelessly using Bluetooth to an Android smartphone. This project will not only help informal caregivers in developed countries and further advance the field of telemedicine, but might also have applications in third world countries due to its low cost assembly.

Candidate: 

Aliaa Ahmed Bassim Aly Mohamed Doma

Supervisor: 

Daniel Haak
Examiner: 

Prof. Dr.-Ing. Thomas Deserno,
Dr.rer.medic. Stephan Jonas

From --- to 11/2015




Candidate: 

Tim Ix

Supervisor: 

Jó Ágila Bitsch,
Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.-Ing. Klaus Wehrle,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From 10/2015 to 06/2015





Depression is the most prevalent clinical disorder and one of the main causes of dis- ability. This makes early detection of depressive symptoms critical in its prevention and management. This thesis presents and discusses the development of Psycholo- gist in a Pocket (PiaP), a mobile Health application for Android which screens and monitors for these symptoms, and - given the explicit permission of the user - alerts a trusted contact such as the mental health professional or a close friend, if it detects symptoms. All text input electronically—such as short message services, emails, social network posts—is analysed based on keywords related to depression based on DSM-5 and ICD criteria as well as Beck’s Cognitive Theory of Depression and the Self-Focus Model. Data evaluation and collection happen in the background, on-device, without requiring internet connection.

Candidate: 

Ekaterina Sirazitdinova

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.sc.techn. Bastian Leibe,
Prof. Dr.-Ing Thomas Deserno

From --- to 06/2014





A novel approach for guiding visually impaired people with the help of smartphones and image processing techniques is being developed. The aim of the project is to build a robust and affordable tool to provide navigation in outdoor environments. This work is part of this endeavor, where the task of improving 3D models in the image-guided positioning system was explored. In the current state of the system, the result of performing 3D reconstruction from images consists of several models. However, a joint model to provide navigation routes is required. High computational costs cause the necessity of storing and compressing 3D data without loss of precision. Our initial assumption is that the size of the point clouds can be reduced significantly by removing outliers. Two problems, 3D model joining and outlier removal were addressed. We implemented an application module, which takes as input models containing sparse 3D point clouds and their corresponding camera locations, together with camera GPS data. The module reduces the number of outliers in the initial models and estimates a precise geographical position for each. Separate models are aligned in the same coordinate space with the help of freely avail- able geographical digital models. Based on successfully registered models, a combined 3D model is built in order to provide navigational information. The method implemented works well for models with small initial GPS errors. It is also able to register models with an initial GPS error of up to 40m, if they contain sufficient structural information for alignment. For outlier removal, two existing approaches were implemented. Additionally, an own method was proposed. The three methods were evaluated in terms of their influence on localization accuracy. We observed that outlier removal does not improve the positioning accuracy of the system. We also observed that outlier removal does not degrade the quality of localization either, when the number of points removed is not higher than 25- 30% compared to the original model. Thus, we confirmed our initial hypothesis, stating that we can maintain positioning accuracy while reducing the number of points in a reconstructed model.

Candidate: 

Inga Güthe

Supervisor: 

Dr. Antoine Serrurier,
Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.med. Neuschaefer-Rube,
Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From --- to ---





Snoring is a widespread sleep-disorder. Especially middle- or high-aged men are affected with a prevalence of ca. 20 to 45%. Snoring is produced by the relax of the soft parts of the upper respiratory tract during sleep and the subsequent vibration. There are different sources that can produce the snoring. One the one hand snoring can be produced be the velum which especially is relevant for the described clinical trial. On the other hand the root of tongue or the epiglottis can be other sources. Apart from that there are different methods to treat the snoring. These methods include operational methods and additives. Additionally conservative methods such as weight reduction or speech and language therapy can also be used to handle the snoring. In the described clinical trial the attention is focussed upon the speech and language therapeutic treatment. In previous clinicals trials the effect of speech and language therapeutic exercises on subjects suffering from obstructive sleep apnoea or on mixed groups of subjects suffering from obstructive sleep apnoea or from primary snoring was investigated. Apart from that they always included both sexes, although in one study it was shown that there are differences between men and women. Another point is that in these studies they always used the same methods for every kind of snoring. Because of that the aim of the study is to investigate whether a speech and language therapeutic treatment which is laid out on the invigoration of the velum or a speech and language therapeutic treatment which does not focus the velum but includes holistic myofunctional exercises can reduce or eleminate the snoring of men suffering from velar induced primary snoring.

Diploma Theses

Candidate: 

Malte Sartor

Supervisor: 

Dr. Stephan Jonas,
Dr. Tobias Wartzek
Examiner: 

Prof. Dr.-Ing. Dr. med. Steffen Leonhardt,
Dr. rer. nat. Dipl.-Ing. Thomas Deserno

From --- to 05/2014






Eine große Herausforderung bei der Verarbeitung und der Analyse von Langzeit- Elektrokardiogrammen (EKG) ist, dass die sich ändernde Herzrate zu einer unterschiedli- chen Länge der einzelnen Abschnitte der Zyklen führt. Dies macht Vergleiche der einzelnen Zyklen und die Erkennung von statistischen Zusammenhängen schwierig, da viele Cluste- ringverfahren eine konstante Eingangsvektorgröße benötigen. In dieser Arbeit wird ein neues Hilfsmittel zur Analyse dieser Zusammenhänge vorgestellt: die nichtlineare Zeitnormierung für EKG-Daten. Diese passt die Zyklen des EKGs an die Länge eines Referenzzyklus an. Dabei müssen unterschiedliche Verschiebungen und Streckungen der einzelnen Anteile des Signals berücksichtigt werden. Hierdurch kann eine statistische Analyse direkt auf den Signalwerten der Zyklen ausgeführt werden. Bisher existieren keine Veröffentlichungen, außer der im Rahmen dieser Arbeit entstande- nen, die sich mit Zeitnormierungen von EKG-Zyklen beschäftigen, weshalb hier neue Wege beschritten werden müssen. Zur Normierung der EKG-Zyklen werden zwei Verfahren eingeführt: eines auf Basis eines modellbasierten Ansatzes, das Analytical Model Matching (AMM) und ein modellfreies Verfahren, das Dynamic Time Warping (DTW). Zusätzlich werden zur Bewertung der Verfahren Bewertungsmaße erstellt, so dass die Ver- fahren quantitativ verglichen werden können. Diese beschreiben die Ähnlichkeit des nor- mierten Zyklus zu dem Eingangszyklus, sowie zu dem Referenzzyklus. Es werden Experimente mit EKG-Zyklen von gesunden und erkrankten Patienten durch- geführt. Diese werden anhand der Bewertungsmaße ausgewertet. So wird festgestellt, dass beide Verfahren zur nichtlinearen Zeitnormierung geeignet sind, sie jedoch bei den verschie- denen Experimenten unterschiedlich gut abschneiden. Daher muss je nach Einsatzzweck abgewogen werden, welches Verfahren eingesetzt wird.

PhD Theses

Candidate: 

Marko Jovanović

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From 07/2016 to ---




An essential and indispensable part of physiotherapy education presents learning of practical and motoric skills. In traditional classroom settings, the knowledge transfer of manual skills is performed through observation and imitation of demonstrated actions and movements. To be able to gain new competences through a complete and successful transfer of knowledge, it is crucial for students to receive immediate and individual feedback during the learning process. Feedback allows for a timely correction of mistakes and optimization of overall competence. Since only a limited amount of time is available to teachers for demonstration and supervision of individual students, this presents a major challenge in traditional classroom settings. To cater for the needs of both students and teachers, the Media Didactics Meets Wearable Computing (MediWeCo) project aims to develop novel and targeted teaching methods and learning concepts for occupations in which practical and motoric skills play a significant role. A novel mobile blended learning solution combines digital media with innovative wearable sensor-based feedback for teaching and learning of physiotherapeutic skills. Innovative wearable sensors allow for an assessment of manual performance of individual learners. A digital assistant guides, accompanies and evaluates performance of the learner by evaluating the sensor data. The goal of this thesis is to investigate how and to which degree introducing wearable devices can have a positive impact on clinical education of manual skills. Specifically, the introduction of sensor-based individualized direct feedback and objective assessment of skill performance will be evaluated. Therefore, a processing pipeline for MediWeCo is developed and includes a human-to-technology module, which establishes communication between the learning application and the wearable sensor device. The wearable sensors are evaluated for sufficient precision in monitoring necessary gestures and therapeutic movements. In case the wearable does not meet the expectations regarding necessary precision, the catalog of supported therapeutic measures can be adjusted accordingly or, if available, better performing sensors can be used. An approach for personal sensor calibration in terms of rotation and translation of the sensor has to be developed for optimum usability and reproducibility. Test data sets are recorded from a study group performing a set of different physiotherapy techniques from which a gesture model is created. Herefore, a dedicated application for recording movement patterns and data format for has to be developed. A gesture evaluation algorithm based on machine learning methods classifies incoming sensor data against defined movement models in real-time. Another challenge is the selection of optimum features from both a learning aspect (i.e. speed, timing, movement etc.) and from a technical aspect (i.e. characteristics extracted from the signal data for further processing). Prospective trials will be used to evaluate conventional teaching approaches with the proposed approach, and explore further the learning outcome of the novel manual teaching approach.

MD Theses

Candidate: 

Yannic Titgemeyer

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From 03/2016 to ---




Electroencephalograms (EEGs) are an important diagnostic tool in modern medicine. Since it is not always possible to get a patient to an EEG machine, a mobile EEG device would grant medical professionals a new method to get an EEG from such patients, especially in rural or resource-austere areas. Additionally, the use of mobile EEGs can push the boundaries of brain research in collecting data in remote locations which lack access to EEG devices available in clinics or laboratories. The main challenge which needs to be addressed is to assess whether mobile EEG devices are reliable and comparable in signal quality to the traditional EEG machines used for patients. The main task of this thesis is the collection of EEG data with mobile devices from patients and the comparison of the obtained results to a clinical EEG on a quantitative as well as qualitative level. Therefore, the procedure of the mobile EEG data collection should be as equal as possible to the traditional procedure. We assume to find evidence that mobile devices for EEGs are a good alternative to the traditional ones and supply sufficient quality for initial screening purposes, and in further development full diagnostics.

Candidate: 

Heiko Waldmüller

Supervisor: 

Dr. Stephan Jonas
Examiner: 

Univ.-Prof. Dr.med. Dr.rer.nat. Klaus Kabino

From 03/2016 to ---




A system for Electronic Health Records is one of the hardest tasks of our time in the medical field. A strong indicator is, that there is only one country that implemented a EHR-system over the full life extension of citizens (Estonia). Germany is working on a system (eGK, developer: Gematik). There are strong challenges. Identifying these and suggesting a problem-solving approach is the aim of project „LauteKarte“. To identify the ideal communication way and abstraction level „360-delphi“, a stakeholder selected Delphi study method, is developed at the IMIB health division. An EHR-System over lifespan can among others correct especially the mismatch in End-Of-Life-decisions between patients wishes and medical standard procedures.