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Congress Report 2020.1-5
(2020)
Congress Report 2019.12
(2020)
Congress Report 2020.6-8
(2020)
Die Ergebnisse der vorliegenden Synopsis konnten an vergleichsweise hohen Probandenzahlen zeigen, dass die auftretenden Amplituden der Kopfbeschleunigungen stark von der Herangehensweise (Stand, Sprung, Lauf) abhängen. Was zunächst trivial erscheint, ist nun evidenzbasiert und von hoher praktischer Relevanz. Bei der Technikvermittlung sind somit zunächst Kopfballvarianten aus der Standposition vorzuziehen, da diese zu einer geringeren Beschleunigung des Kopfes führen als Varianten welche mit einem Anlauf gekoppelt werden (Stand vs. Sprung). Kopfballvarianten mit einem erhöhten koordinativen Anforderungsprofil (Sprung) führen nicht zwangsläufig zu einer erhöhten Kopfbeschleunigung, sollten jedoch aus methodischen Gründen trotzdem zu einem späteren Zeitpunkt trainiert werden. Das eingesetzte Kopfballpendel führte zu Kopfbeschleunigungen zwischen 5.2 und 7.8 G. Diese Werte liegen deutlich unterhalb derer, die bei beschleunigten Bällen gemessen werden, was für den Einsatz des Kopfballpendels bei der Technikschulung spricht. Der Rumpfmuskulatur wird eine große Bedeutung bei der technischen Umsetzung von Kopfbällen zugesprochen [120]. Die vorliegenden Ergebnisse konnten jedoch keinen Anstieg der Kopfbeschleunigung nach Ermüdung der Rumpfmuskulatur darlegen. Ein Pre-Post-Vergleich bei beschleunigten Bällen muss folgen, um dies weiterführend untersuchen zu können. Vergleichbare Ergebnisse und Interpretationen liegen nun zur Wirksamkeit einer 6-wöchigen Hals-Nackenkräftigung vor, welche bei dem statischen Kopfballpendel keine Änderungen der Kopfbeschleunigung zur Folge hatten. Kritisch reflektiert werden müssen insbesondere die Art und Dauer sowie die Inhalte einer solchen Intervention. Dennoch steckt hinter dieser Hypothese weiterhin ein vielversprechender Ansatz das Kopfballspiel sicherer zu machen. Die Ausrichtung des Kopf-Hals-Rumpfsegmentes steht in keinem direkten Zusammenhang zur resultierenden Beschleunigung des Kopfes, wonach eine erhöhte Nickbewegung nicht mit einer erhöhten Kopfbeschleunigung korreliert. Im nächsten Schritt muss ein intraindividueller Vergleich vorgenommen werden, da die Kompensationsmechanismen höchst individuell sind. Außerdem sollte zukünftig die maximale Kopfbeschleunigung - unabhängig von dem Zeitpunkt - mit dem Winkel (Kopf, HWS) während des ersten Ballkontaktes verglichen werden, statt den zeitsynchronen Vergleich des Winkels und der in diesem Moment messbaren Kopfbeschleunigung vorzunehmen.
Whole-body electromyostimulation (WB-EMS) is an extension of the EMS application known in physical therapy. In WB-EMS, body composition and skinfold thickness seem to play a decisive role in influencing the Ohmic resistance and therefore the maximum intensity tolerance. That is why the therapeutic success of (WB-)EMS may depend on individual anatomical parameters. The aim of the study was to find out whether gender, skinfold thickness and parameters of body composition have an influence on the maximum intensity tolerance in WB-EMS. [Participants and Methods] Fifty-two participants were included in the study. Body composition (body impedance, body fat, fat mass, fat-free mass) and skinfold thicknesses were measured and set into relation to the maximum intensity tolerance. [Results] No relationship between the different anthropometric parameters and the maximum intensity tolerance was detected for both genders. Considering the individual muscle groups, no similarities were found in the results. [Conclusion] Body composition or skinfold thickness do not seem to have any influence on the maximum intensity tolerance in WB-EMS training. For the application in physiotherapy this means that a dosage of the electrical voltage within the scope of a (WB-) EMS application is only possible via the subjective feedback (BORG Scale).
The difference in the efficacy of altered stimulation parameters in whole-body-electromyostimulation (WB-EMS) training remains largely unexplored. However, higher impulse frequencies (>50 Hz) might be most adequate for strength gain. The aim of this study was to analyze potential differences in sports-related performance parameters after a 10-week WB-EMS training with different frequencies. A total of 51 untrained participants (24.9 ± 3.9 years, 174 ± 9 cm, 72.4 ± 16.4 kg, BMI 23.8 ± 4.1, body fat 24.7 ± 8.1 %) was randomly divided into three groups: one inactive control group (CON) and two training groups. They completed a 10-week WB-EMS program of 1.5 sessions/week, equal content but different stimulation frequencies (training with 20 Hz (T20) vs. training with 85 Hz (T85)). Before and after intervention, all participants completed jumping (Counter Movement Jump (CMJ), Squat Jump (SJ), Drop Jump (DJ)), sprinting (5m, 10m, 30m), and strength tests (isometric trunk flexion/extension). One-way ANOVA was applied to calculate parameter changes. Post-hoc least significant difference tests were performed to identify group differences. Significant differences were identified for CMJ (p = 0.007), SJ (p = 0.022), trunk flexion (p = 0.020) and extension (p=.013) with significant group differences between both training groups and CON (not between the two training groups T20 and T85). A 10-week WB-EMS training leads to significant improvements of jump and strength parameters in untrained participants. No differences could be detected between the frequencies. Therefore, both stimulation frequencies can be regarded as adequate for increasing specific sport performance parameters. Further aspects as regeneration or long term effects by the use of different frequencies still need to be clarified.
Nowadays a large part of communication is taking place on social media platforms such as Twitter, Facebook, Instagram, or YouTube, where messages often include multimedia contents (e.g., images, GIFs or videos). Since such messages are in digital form, computers can in principle process them in order to make our lives more convenient and help us overcome arising issues. However, these goals require the ability to capture what these messages mean to us, that is, how we interpret them from our own subjective points of view. Thus, the main goal of this dissertation is to advance a machine's ability to interpret social media contents in a more natural, subjective way.
To this end, three research questions are addressed. The first question aims at answering "How to model human interpretation for machine learning?" We describe a way of modeling interpretation which allows for analyzing single or multiple ways of interpretation of both humans and computer models within the same theoretic framework. In a comprehensive survey we collect various possibilities for such a computational analysis. Particularly interesting are machine learning approaches where a single neural network learns multiple ways of interpretation. For example, a neural network can be trained to predict user-specific movie ratings from movie features and user ID, and can then be analyzed to understand how users rate movies. This is a promising direction, as neural networks are capable of learning complex patterns. However, how analysis results depend on network architecture is a largely unexplored topic. For the example of movie ratings, we show that the way of combining information for prediction can affect both prediction performance and what the network learns about the various ways of interpretation (corresponding to users).
Since some application-specific details for dealing with human interpretation only become visible when going deeper into particular use-cases, the other two research questions of this dissertation are concerned with two selected application domains: Subjective visual interpretation and gang violence prevention. The first application study deals with subjectivity that comes from personal attitudes and aims at answering "How can we predict subjective image interpretation one would expect from the general public on photo-sharing platforms such as Flickr?" The predictions in this case take the form of subjective concepts or phrases. Our study on gang violence prevention is more community-centered and considers the question "How can we automatically detect tweets of gang members which could potentially lead to violence?" There, the psychosocial codes aggression, loss and substance use serve as proxy to estimate the subjective implications of online messages.
In these two distinct application domains, we develop novel machine learning models for predicting subjective interpretations of images or tweets with images, respectively. In the process of building these detection tools, we also create three different datasets which we share with the research community. Furthermore, we see that some domains such as Chicago gangs require special care due to high vulnerability of involved users. This motivated us to establish and describe an in-depth collaboration between social work researchers and computer scientists. As machine learning is incorporating more and more subjective components and gaining societal impact, we have good reason to believe that similar collaborations between the humanities and computer science will become increasingly necessary to advance the field in an ethical way.
Problems, Chances and Limitations of Facilitating Self-Directed Learning at a German Gymnasium
(2020)
Self-directed learning is becoming more important than ever. In a rapidly changing world, learners must be ready to face new obstacles. Self-directed learning gives the learners the chance to adapt to these social contextual changes. But facilitating self-directed learning in formal settings seems to be a risky task and venture. To accomplish its facilitation, many limits must be overcome.
In this thesis, lessons at a German school called a Gymnasium – the type of school where learners can get the highest school level degree – were observed in order to find out in how far elements of self-directed learning can be found in the observed lessons. For the comparison, the process elements of Knowles’ book “Self-Directed Learning: A Guide for Learners and Teachers” from 1975 were adapted to the observations of the lessons.
A central part of the observations and interviews of the teachers was to find out which limitations in the facilitation of self-directed learning can be found in terms of the institutional framework and the attitude of the teachers. The results of the observations highly differentiated. Whereas in many of the observed scientific lessons, many elements of self-directed learning were found, the lessons in social studies were teacher-directed. Also, a different attitude between the teachers was found in terms of the support for self-directed learning.
Importantly, the thesis includes the scientific critic of self-directed learning instead of excluding it and proposes the facilitation of Grow’s “Self-Directed-Learning Model” (1991) where the level of the learner’s self-directed learning is supposed to progress during school. This thesis is relevant for educators, curriculum developers, teachers and policymakers to help them identify the difficulties and chances to facilitate SDL in formal settings.
Dieser Arbeits- und Forschungsbericht beschreibt die Analyse der Wirkung der durch das Teil- projekt etablierten (Weiter-)Bildungsangebote an der Hochschule Kaiserslautern. Zu diesem Zweck wurden sowohl in der Programmentwicklung beteiligte Unternehmen als auch die Stu- dierenden, welche die entwickelten Angebote derzeit wahrnehmen, befragt. Ziel war es, unter anderem die Zielgruppenerreichung, einen Outcome sowie einen Impact messbar zu machen und zu analysieren. Aus den gewonnen Ergebnissen lassen sich Rückschlüsse auf die anvi- sierte Wirkung der (Weiter-)Bildungsangebote zum einen auf Studierende und zum anderen auf die in der Region ansässigen Unternehmen ziehen. Gleichzeitig wird der Bekanntheitsgrad der Hochschule Kaiserslautern als Bildungsträgerin analysiert und wichtige Erkenntnisse für die nachhaltige Qualitätssicherung der wissenschaftlichen (Weiter-)Bildung gezogen.
Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns
