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Wearable sensor technology already has a great impact on the endurance running community. Smartwatches and heart rate monitors are heavily used to evaluate runners&rsquo, performance and monitor their training progress. Additionally, foot-mounted inertial measurement units (IMUs) have drawn the attention of sport scientists due to the possibility to monitor biomechanically relevant spatio-temporal parameters outside the lab in real-world environments. Researchers developed and investigated algorithms to extract various features using IMU data of different sensor positions on the foot. In this work, we evaluate whether the sensor position of IMUs mounted to running shoes has an impact on the accuracy of different spatio-temporal parameters. We compare both the raw data of the IMUs at different sensor positions as well as the accuracy of six endurance running-related parameters. We contribute a study with 29 subjects wearing running shoes equipped with four IMUs on both the left and the right shoes and a motion capture system as ground truth. The results show that the IMUs measure different raw data depending on their position on the foot and that the accuracy of the spatio-temporal parameters depends on the sensor position. We recommend to integrate IMU sensors in a cavity in the sole of a running shoe under the foot&rsquo, s arch, because the raw data of this sensor position is best suitable for the reconstruction of the foot trajectory during a stride.

Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 &plusmn, 0.274 m/s for the velocity estimation and 0.022 &plusmn, 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.

Wearable athlete support systems are a popular technology for performance enhancement in sports. The complex signal and data analysis on these systems is often tackled with pattern recognition techniques like classification. The implementation of classification algorithms on mobile hardware is called embedded classification. Thereby, technical challenges arise from the restricted computational power, battery capacity and size of such mobile systems. The accuracy-cost tradeoff describes the two conflicting design goals of embedded classification systems; the accuracy and the computational cost of a classifier. A data analysis system should be as accurate as possible and, at the same time, as cheap as required for an implementation on a mobile system with restricted resources. Thus, accuracy and cost have to be simultaneously considered in the design phase of the algorithms. Furthermore, an accurate modeling of classification systems, a precise estimation of the expected accuracy and energy efficient algorithms are needed. The first main goal of this thesis was to develop design considerations to support the solution of the accuracy-cost tradeoff that occurs during embedded classification system design. The success of wearable technology in sports originates in several application opportunities. A wearable system can collect data in the field without the restrictions of a lab environment or capture volume. Data with realistic variation and a high number of trials can be used to analyze the true field performance as well as its long-term progress over time. Real-time data processing allows to support athletes and coaches with augmented feedback in the field. Therefore, wearable systems enable new opportunities for the analysis of athletic performance. The second main goal of this thesis was to illustrate the benefits and opportunities of wearable athlete support systems with three application examples. This thesis presents applications for plyometric training, golf putting and swimming. The applications were realized as body sensor networks (BSNs) and analyzed kinematic data that were acquired with inertial measurement units (IMUs). The plyometric training application targeted the ground contact time calculation in drop jump exercises. The presented algorithm used a hidden markov model to calculate the ground contact with high accuracy. The ground contact time is an important training parameter to assess athlete performance and cannot be visually assessed. The golf putt application realized a system for technique training in the field. It featured an algorithm for automatic putt detection and parameter extraction as a basis for augmented feedback training applications. Long-term kinematic golf training data provided new insights in the progress of golf putting. The swimming application realized an unobtrusive swimming exercise tracker. The system was able to classify the four swimming styles, turns and breaks based on the head kinematics. Furthermore, an algorithm to classify long-term fatigue that occurs in a 90 min swimming exercise was presented. The swimming style, turn and break classification was implemented as embedded classification system on a BSN sensor node. This application underlines the applicability of the presented design considerations for solving the accuracy-cost tradeoff. The contributions of this thesis are considerations for the design of embedded classification systems and application examples that illustrate the benefits of wearable athlete support systems. The focus was to provide practical support for the solution of the accuracy-cost tradeoff and to present the technical realization of wearable athlete support systems. The findings of this thesis can be beneficial for research as well as industrial applications in the sport, health and medical domain. Tragbare Sportler-Assistenz-Systeme sind eine weit verbreitete Technologie zur Leistungssteigerung im Sport. Die komplexe Signal- und Datenverarbeitung auf diesen Systemen wird dabei oft mit Methoden der Mustererkennung, wie Klassifikation, bewerkstelligt. Die Implementierung von Klassifikationsalgorithmen auf mobiler Hardware nennt man eingebettete Klassifikation. Technische Herausforderungen entstehen hierbei durch die limitierte Rechenleistung, Batteriekapazität und Größe mobiler Systeme. Der Accuracy-Cost-Tradeoff beschreibt die beiden konkurrierenden Entwurfsziele von eingebetteten Klassifikationssystemen: die Genauigkeit und die Berechnungskosten eines Klassifikators. Ein Datenanalyse-System soll die maximale Genauigkeit erreichen und gleichzeitig auf einem mobilen System mit limitierten Ressourcen implementiert werden können. Genauigkeit und Kosten müssen daher in der Entwurfsphase von Algorithmen parallel betrachtet werden. Zudem ist es nötig, Klassifikationssysteme akkurat zu Modellieren, ihre zu erwartende Genauigkeit präzise abzuschätzen und ihre Energieeffizienz im Entwurf zu berücksichtigen. Das erste Ziel dieser Arbeit war es, die Lösung des Accuracy-Cost-Tradeoffs beim Entwurf eingebetteter Klassifikationssysteme mit verschiedenen Gesichtspunkten zu unterstützen. Der Erfolg tragbarer Systeme im Sport basiert auf verschiedenen Chancen dieser Technologie. So kann ein tragbares System Felddaten aufzeichnen und ist nicht auf eine Laborumgebung oder einen Messplatz beschränkt. Es kann eine große Datenmenge mit realitätsnaher Variation genutzt werden, um die Leistung im Feld und deren Langzeitverlauf zu analysieren. Die Verarbeitung der Daten in Echtzeit erlaubt es, den Sportler und den Trainer im Feld mit extrinsischem Feedback zu unterstützen. Tragbare Systeme eröffnen daher neue Möglichkeiten sportliche Leistung zu analysieren. Das zweite Ziel dieser Arbeit war es, diese Möglichkeiten anhand von drei Anwendungen zu verdeutlichen um den Mehrwert der Systeme zu unterstreichen. In dieser Arbeit werden mobile Anwendungen für plyometrisches Training, Golf Putting und Schwimmen vorgestellt. Sie bestehen aus Netzwerken drahtloser, körpernaher Inertialsensoren mittels derer kinematische Daten mobil verarbeitet werden können. Die Anwendung für plyometrisches Training berechnete die Bodenkontaktzeit bei Niedersprügen. Der Datenanalyse-Algorithmus verwendete dabei ein Hidden Markov Modell, um den auftretenden Bodenkontakt mit hoher Genauigkeit zu ermitteln. Die Bodenkontaktzeit ist ein wichtiger Trainingsparameter zur Beurteilung der Sprungleistung und visuell nicht zu ermitteln. Die Golf Putting Anwendung beschreibt ein System zum Techniktraining beim Golf. Der entwickelte Algorithmus zur automatischen Erkennung von Schlägen und Berechnung von Schlagparametern stellt die Basis für Anwendungen mit extrinsischem Feedback dar. Langzeitaufzeichnungen von kinematischen Daten während des Golftrainings lieferten im Rahmen dieser Arbeit neue Einblicke in die Entwicklung der Schlagtechnik. Die Schwimmanwendung stellt ein dezentes System zur Trainingsüberwachung vor. Es war in der Lage die vier verschiedenen Schwimmstile sowie Wenden und Pausen anhand der Kopfkinematik zu unterscheiden. Außerdem konnte mit dem System die Langzeitermüdung eines 90-minütigen Schwimmtrainings erkannt werden. Die Erkennung der Schwimmstile, Wenden und Pausen wurde als eingebettetes Klassifikationsystem auf einem tragbaren Sensorknoten implementiert. Diese Anwendung unterstreicht daher die Eignung der entwickelten Gesichtspunkte zur Lösung des Accuracy-Cost-Tradeoffs. Diese Arbeit stellt verschiedene Gesichtspunkte zum Entwurf eingebetteter Klassifikationssysteme vor und unterstreicht anhand von Anwendungsbeispielen den Mehrwert tragbarer Sportler-Assistenz-Systeme. Der Fokus lag auf einer anwendungsorientierten Unterstützung zur Lösung des Accuracy-Cost-Tradeoffs und auf der technischen Umsetzung tragbarer Sportler-Assistenz-Systeme. Von den Erkenntnissen dieser Arbeit können sowohl Forschung als auch Industrie aus den Bereichen Sport, Gesundheit und Medizin profitieren.

BACKGROUND: Some concerns still have not been resolved about the long-term safety of drug-eluting stents (DES) in patients with acute STEMI. OBJECTIVES: The aim of this study was to evaluate the stent thrombosis (ST) rate up to 3 years in patients with ST-segment elevation myocardial infarction (STEMI) treated by primary percutaneous coronary intervention (PCI) with new-generation drug-eluting stents (n-DES) compared with bare-metal stents (BMS) and old-generation drug-eluting stents (o-DES) enrolled in the SCAAR (Swedish Coronary Angiography and Angioplasty Registry). METHODS: From January 2007 to January 2013, 34,147 patients with STEMI were treated by PCI with n-DES (n = 4,811), o-DES (n = 4,271), or BMS (n = 25,065). The risks of early/late (up to 1 year) and very late definite ST (after 1 year) were estimated. RESULTS: Cox regression landmark analysis showed a significantly lower risk of early/late ST in patients treated with n-DES (hazard ratio [HR]: 0.65; 95% confidence interval [CI]: 0.43 to 0.99; p = 0.04) and o-DES (HR: 0.60; 95% CI: 0.41 to 0.89; p = 0.01) compared with the BMS group. The risk of very late ST was similar between the n-DES and BMS groups (HR: 1.52; 95% CI: 0.78 to 2.98; p = 0.21), whereas a higher risk of very late ST was observed with o-DES compared with BMS (HR: 2.88; 95% CI: 1.70 to 4.89; p < 0.01). CONCLUSIONS: Patients treated with n-DES have a lower risk of early/late ST than patients treated with BMS. The risk of very late ST is low and comparable between n-DES and BMS up to 3 years of follow-up, whereas o-DES treatment is associated with an increased risk of very late ST. The current STEMI guidelines might require an update in light of the results of this and other recent studies.

Der Text gliedert sich in drei Teile; zunächst geht es um die Geschichte der VHS-Kassette, dann werden exemplarisch einige kunsthistorische Filme, die in der Sammlung der Mediathek auf VHS vorliegen, stilistisch untersucht. Der dritte Teil untersucht die Provenienz von Werken der Videokunst.