Lebenswissenschaften und Ethik
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Preliminary considerations on the form-finding of a tensegrity joint to be used in dynamic orthoses
(2024)
Early motion therapy plays an important role for effective long-term healing of joint injuries. In many cases, conventional dynamic orthoses fail to address the intricate movement possibilities of the underlying joints, limited by their simplistic joint representations, often represented by revolute joints, enabling rotations by only one axis. In this paper, a two-dimensional compliant tensegrity joint for use in biomedical applications is investigated. It consists of two compressed members and five compliant tensioned members. Relative movement possibilities are realized by the intrinsic compliance of the structure. In the development of these systems, the first step is the determination of the static stable equilibrium. This analysis is conducted in this paper by considering the potential energy approach or by using the geometric nonlinear finite element method. The mechanical behavior of the structure is assessed with a specific emphasis on its mechanical compliance. The primary objective of this study is the investigation of the influence of structural parameters on the overall stiffness and movability of the structure. The results underscore the significant effect of member parameters on the stiffness and movability of the compliant tensegrity joint, particularly under varying load magnitudes. These findings provide insights for optimizing the joint’s performance, contributing to its potential application in advanced orthotic and exoskeleton devices.
Resting motor threshold and magnetic field output of the figure-of-8 and the double-cone coil
(2020)
The use of the double-cone (DC) coil in transcranial magnetic stimulation (TMS) is promoted with the notion that the DC coil enables stimulation of deeper brain areas in contrast to conventional figure-of-8 (Fo8) coils. However, systematic comparisons of these two coil types with respect to the spatial distribution of the magnetic field output and also to the induced activity in superficial and deeper brain areas are limited. Resting motor thresholds of the left and right first dorsal interosseous (FDI) and tibialis anterior (TA) were determined with the DC and the Fo8 coil in 17 healthy subjects. Coils were orientated over the corresponding motor area in an angle of 45 degrees for the hand area with the handle pointing in posterior direction and in medio-lateral direction for the leg area. Physical measurements were done with an automatic gantry table using a Gaussmeter. Resting motor threshold was higher for the leg area in contrast to the hand area and for the Fo8 in contrast to the DC coil. Muscle by coil interaction was also significant providing higher differences between leg and hand area for the Fo8 (about 27%) in contrast to the DC coil (about 15%). Magnetic field strength was higher for the DC coil in contrast to the Fo8 coil. The DC coil produces a higher magnetic field with higher depth of penetration than the figure of eight coil.
Objectives
The European University Association (EUA) defines “employability” as a major goal of higher education. Therefore, competence-based orientation is an important aspect of education. The representation of a standardized job profile in the field of medical informatics, which is based on the most common labor market requirements, is fundamental for identifying and conveying the learning goals corresponding to these competences.
Methods
To identify the most common requirements, we extracted 544 job advertisements from the German job portal, STEPSTONE. This process was conducted via a program we developed in R with the “rvest” library, utilizing web crawling, web extraction, and text mining. After removing duplicates and filtering for jobs that required a bachelor's degree, 147 job advertisements remained, from which we extracted qualification terms. We categorized the terms into six groups: professional expertise, soft skills, teamwork, processes, learning, and problem-solving abilities.
Results
The results showed that only 45% of the terms are related to professional expertise, while 55% are related to soft skills. Studies of employee soft skills have shown similar results. The most prevalent terms were programming, experience, project, and server. Our second major finding is the importance of experience, further underlining how essential practical skills are.
Conclusions
Previous studies used surveys and narrative descriptions. This is the first study to use web crawling, web extraction, and text mining. Our research shows that soft skills and specialist knowledge carry equal weight. The insights gained from this study may be of assistance in developing curricula for medical informatics.
Embodiment theories have proposed a reciprocal relationship between emotional state and bodily reactions. Besides large body postures, recent studies have found emotions to affect rather subtle bodily expressions, such as slumped or upright sitting posture. This study investigated back muscle activity as an indication of an effect of positive and negative emotions on the sitting position. The electromyography (EMG) activity of six back muscles was recorded in 31 healthy subjects during exposure to positive and negative affective pictures. A resting period was used as a control condition. Increased muscle activity patterns in the back were found during the exposure to negative emotional stimuli, which was mainly measured in the lumbar and thorax regions. The positive emotion condition caused no elevated activity. The findings show that negative emotions lead to increased differential muscle activity in the back and thus corroborate those of previous research that emotion affects subtle bodily expressions.
Aims
VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images.
Methods
858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”.
Results
Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable.
Conclusions
The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases.
Clinical setting
Third space procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex minimally invasive techniques with an elevated risk for operator-dependent adverse events such as bleeding and perforation. This risk arises from accidental dissection into the muscle layer or through submucosal blood vessels as the submucosal cutting plane within the expanding resection site is not always apparent. Deep learning algorithms have shown considerable potential for the detection and characterization of gastrointestinal lesions. So-called AI – clinical decision support solutions (AI-CDSS) are commercially available for polyp detection during colonoscopy. Until now, these computer programs have concentrated on diagnostics whereas an AI-CDSS for interventional endoscopy has not yet been introduced. We aimed to develop an AI-CDSS („Smart ESD“) for real-time intra-procedural detection and delineation of blood vessels, tissue structures and endoscopic instruments during third-space endoscopic procedures.
Characteristics of Smart ESD
An AI-CDSS was invented that delineates blood vessels, tissue structures and endoscopic instruments during third-space endoscopy in real-time. The output can be displayed by an overlay over the endoscopic image with different modes of visualization, such as a color-coded semitransparent area overlay, or border tracing (demonstration video). Hereby the optimal layer for dissection can be visualized, which is close above or directly at the muscle layer, depending on the applied technique (ESD or POEM). Furthermore, relevant blood vessels (thickness> 1mm) are delineated. Spatial proximity between the electrosurgical knife and a blood vessel triggers a warning signal. By this guidance system, inadvertent dissection through blood vessels could be averted.
Technical specifications
A DeepLabv3+ neural network architecture with KSAC and a 101-layer ResNeSt backbone was used for the development of Smart ESD. It was trained and validated with 2565 annotated still images from 27 full length third-space endoscopic videos. The annotation classes were blood vessel, submucosal layer, muscle layer, electrosurgical knife and endoscopic instrument shaft. A test on a separate data set yielded an intersection over union (IoU) of 68%, a Dice Score of 80% and a pixel accuracy of 87%, demonstrating a high overlap between expert and AI segmentation. Further experiments on standardized video clips showed a mean vessel detection rate (VDR) of 85% with values of 92%, 70% and 95% for POEM, rectal ESD and esophageal ESD respectively. False positive measurements occurred 0.75 times per minute. 7 out of 9 vessels which caused intraprocedural bleeding were caught by the algorithm, as well as both vessels which required hemostasis via hemostatic forceps.
Future perspectives
Smart ESD performed well for vessel and tissue detection and delineation on still images, as well as on video clips. During a live demonstration in the endoscopy suite, clinical applicability of the innovation was examined. The lag time for processing of the live endoscopic image was too short to be visually detectable for the interventionist. Even though the algorithm could not be applied during actual dissection by the interventionist, Smart ESD appeared readily deployable during visual assessment by ESD experts. Therefore, we plan to conduct a clinical trial in order to obtain CE-certification of the algorithm. This new technology may improve procedural safety and speed, as well as training of modern minimally invasive endoscopic resection techniques.
ARTIFICIAL INTELLIGENCE (AI) – ASSISTED VESSEL AND TISSUE RECOGNITION IN THIRD-SPACE ENDOSCOPY
(2022)
Aims
Third-space endoscopy procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex interventions with elevated risk of operator-dependent adverse events, such as intra-procedural bleeding and perforation. We aimed to design an artificial intelligence clinical decision support solution (AI-CDSS, “Smart ESD”) for the detection and delineation of vessels, tissue structures, and instruments during third-space endoscopy procedures.
Methods
Twelve full-length third-space endoscopy videos were extracted from the Augsburg University Hospital database. 1686 frames were annotated for the following categories: Submucosal layer, blood vessels, electrosurgical knife and endoscopic instrument. A DeepLabv3+neural network with a 101-layer ResNet backbone was trained and validated internally. Finally, the ability of the AI system to detect visible vessels during ESD and POEM was determined on 24 separate video clips of 7 to 46 seconds duration and showing 33 predefined vessels. These video clips were also assessed by an expert in third-space endoscopy.
Results
Smart ESD showed a vessel detection rate (VDR) of 93.94%, while an average of 1.87 false positive signals were recorded per minute. VDR of the expert endoscopist was 90.1% with no false positive findings. On the internal validation data set using still images, the AI system demonstrated an Intersection over Union (IoU), mean Dice score and pixel accuracy of 63.47%, 76.18% and 86.61%, respectively.
Conclusions
This is the first AI-CDSS aiming to mitigate operator-dependent limitations during third-space endoscopy. Further clinical trials are underway to better understand the role of AI in such procedures.
Aims
AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy.
Methods
5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels.
Results
Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM).
Conclusions
Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy.
Aims
While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM.
Methods
A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy.
Results
The AI algorithm had a mean VDR of 93.5% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9% to 73.0% and from 59.0% to 74.1% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec).
Conclusions
AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology.
Aims
Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel.
Methods
Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined.
Results
The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference.
Conclusions
We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time.
Background and aims
Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance.
Methods
A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement.
Results
External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable.
Conclusion
In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases.
Aims
Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD.
Methods
A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames.
Results
The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters.
Conclusions
The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.
This is a report on a course taught at OTH Regensburg in the summer term of 2018. The students in this course built their own SQL-on-Hadoop engine as a term project in just 8 weeks. miniHive is written in Python and compiles SQL queries into MapReduce workflows. These are then executed on Hadoop. miniHive performs generic query optimizations (selection and projection pushdown, or cost-based join reordering), as well as MapReduce-specific optimizations.
The course was taught in English, using a flipped classroom model. The course material was mainly compiled from third-party teaching videos. This report describes the course setup, the miniHive milestones, and gives a short review of the most successful student projects.
Purpose
The purpose of this study was to gather data about physical activity and quality of life (QoL) for people of working age with visual impairments to optimize upcoming physical activity-based interventions in vocational rehabilitation.
Methods
Two hundred and seventy-seven former participants of four vocational rehabilitation centres in Germany answered an online questionnaire. The health-related QOL has been assessed by means of the WHOQOL-BREF questionnaire. The data were analysed via multivariate analysis of covariances (MANCOVAs), univariate analysis of covariances (ANCOVAs) and discriminant function analysis.
Results
Compared to a normative group of adults aged 36-45, the study group showed lower values in all domains of QOL. The MANCOVA (visual acuity) produced no multivariate significant effect. Also, the ANCOVA showed no significant effect for the global dimension of the WHOQOL-BREF. The MANCOVA (leisure time activity) produced a multivariate significant effect. Post-hoc ANCOVAS revealed significant effects for all four domains of QoL. The ANCOVA analysing the global domain showed a similar significant effect as well. The active persons had higher values in all domains of QoL compared to the more passive participants.
Conclusion
Interventions in vocational rehabilitation should focus on leisure time activity to enhance QoL. At this stage, the role of physical activity is still unclear and further studies are needed.
Implications for rehabilitation People with visual impairments often show a worse quality of life than normal-sighted people.
The degree of severity of the visual impairment does not affect quality of life.
Sport and physical activity are effective means of improving quality of life.
An active leisure time activity supports the improvement of the quality of life of visual-impaired people that participated in a vocational rehabilitation.
The purpose of this study was to empirically analyze the sports background, personality dimensions, attitudes, and social competencies of adult head coaches and young assistant coaches involved in the German Einfach Fußball ( Just Soccer) program, which promotes the participation of pupils with intellectual disabilities in soccer/sports and society. Methods. The study recruited 28 head coaches and 29 assistant coaches who completed a questionnaire battery of standardized instruments (NEO Five-Factor Inventory, Interpersonal Reactivity Index, Social Self-Efficacy) as well as self-developed instruments. Analysis of the data involved descriptive and inferential statistical procedures. A descriptive comparison of the assistant coaches with a normative sample of males aged 16–20 years was performed. Results. The head coaches were found with little soccer/sports experience with persons with disabilities prior to participation in the Just Soccer program. However, the majority were familiar with these persons through personal/vocational contacts. Overall, the head coaches were differentiated by formal coaching levels and playing backgrounds, with very few holding any additional formal qualifications in special education. The assistant coaches pre
sented below average scores in the analyzed five personality dimensions when compared with the normative sample. Their attitudes
and social competencies did not change during their 8-month involvement in Just Soccer . Conclusions. The findings highlight
the important role of the coaching staff in the success of the Just Soccerprogram. coaches involved in such activities should be familiarized with needs of people with disabilities, be stress-resistant, and possess a balanced set of personality traits. In addition, the results suggest that such individuals should be coaches/players from conventional soccer clubs instead of special school physical education teachers.
esearch was and still is involved in the controversial issue about innate talent or extensive practice as the determinants of excellent performance in a range of domains. This study aims to contribute by presenting an analysis of practice activities in a domain that appears to be particularly suitable—orchestral conducting. Most conductors usually attain expertise in instrument playing prior to commencing conducting studies. Twenty-seven students of German study programs of orchestral conducting (approximately 18.7% of the population) responded to a questionnaire about their practice activities in conducting programs and their instrumental experiences. Descriptive results show the wealth of prestudy experiences conducting students have. A clear influence on practice activities cannot be stated. During study, students rated conducting-specific practice activities as more demanding and devoted more time to them than to general music practice activities. Therefore, conducting-specific practice activities might have been practiced more deliberately than general music practice activities.
BACKGROUND:
Construct stiffness affects healing of bones fixed with locking plates. However, variable construct stiffness reported in the literature may be attributable to differing test configurations and direct comparisons may clarify these differences.
QUESTIONS/PURPOSES:
We therefore asked whether different distal femur locking plate systems and constructs will lead to different (1) axial and rotational stiffness and (2) fatigue under cyclic loading.
METHODS:
We investigated four plate systems for distal femur fixation (AxSOS, LCP, PERI-LOC, POLYAX) of differing designs and materials using bone substitutes in a distal femur fracture model (OTA/AO 33-A3). We created six constructs of each of the four plating systems. Stiffness under static and cyclic loading and fatigue under cyclic loading were measured.
RESULTS:
Mean construct stiffness under axial loading was highest for AxSOS (100.8 N/mm) followed by PERI-LOC (80.8 N/mm) and LCP (62.6 N/mm). POLYAX construct stiffness testing showed the lowest stiffness (51.7 N/mm) with 50% stiffness of AxSOS construct testing. Mean construct stiffness under torsional loading was similar in the group of AxSOS and PERI-LOC (3.40 Nm/degree versus 3.15 Nm/degree) and in the group of LCP and POLYAX (2.63 Nm/degree versus 2.56 Nm/degree). The fourth load level of > 75,000 cycles was reached by three of six AxSOS, three of six POLYAX, and two of six PERI-LOC constructs. All others including all LCP constructs failed earlier.
CONCLUSIONS:
Implant design and material of new-generation distal femur locking plate systems leads to a wide range of differences in construct stiffness.
CLINICAL RELEVANCE:
Assuming construct stiffness affects fracture healing, these data may influence surgical decision-making in choosing an implant system.
Background
Iliac crest bone harvesting is a frequently performed surgical procedure widely used to treat bone defects. The objective of this study is to assess the biomechanical quantities related to risk for pelvic fracture after harvesting an autologous bone graft at the anterior iliac crest.
Methods
Finite element models with a simulated harvest site (sized 15 × 20 mm, 15 × 35 mm, 30 × 20 mm and 30 × 35 mm) in the iliac wing are created. The relevant loading case is when the ipsilateral leg is lifted off the ground. Musculoskeletal analysis is utilized to compute the muscle and joint forces involved in this motion. These forces are used as boundary conditions for the finite element analyses. Bone tissue stress is analyzed.
Results
Critical stress peaks are located between the anterior superior iliac spine (ASIS) and the anterior edge of the harvest site. Irrespective of the graft size, the iliac wing does not show any significant stress peaks with the harvest site being 20 to 25 mm posterior to the ASIS. The harvest area itself inhibits the distribution of the forces applied on the ASIS to extend to the posterior iliac wing. This leads to a lack of stress posterior to the harvest site. A balanced stress distribution with no stress peaks appears when the bone graft is taken below the iliac crest.
Conclusion
A harvest site located at least 20 to 25 mm posterior to the ASIS should be preferred to minimize the risk of iliac fatigue fracture.
Experts predict that more IT-Security specialists will be needed in the coming years, but in current higher education in engineering disciplines, this topic is hardly addressed. Newer learning methods such as game-based learning (GBL) are enjoying an increasing popularity as their improvement in the education of subject specific topics can be proven by a variety of studies. We chose Educational Escape Rooms (EduER) as a GBL-tool to impart IT-Security in higher education of engineers. In our Escape Room (ER), the students try to solve puzzles and riddles with learned knowledge on the emphasis of cryptography. This paper first deals with a brief introduction to GBL and EduERs, followed by the design of our ER concept, containing different tasks with focus on the topic of cryptography. The tasks cover different cryptographic methods and hash algorithms, e.g. AES, RSA, SHA3. Afterwards the experimental study is presented. The study of the EduER was carried out with students from the bachelor's program in Electrical Engineering and Information Technology at the OTH Regensburg. The participants were divided into three groups of 5 to 8 persons each. They received a briefing with important information, followed by the ER execution, a debriefing afterwards and an exam-like evaluation sheet to test their learned knowledge. Finally, first basic results are presented.