SensX: About Sensing and Assessment of Complex Human Motion

The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without professional supervision which may lead to suboptimal training success or even serious injuries. We believe, that automated supervision and realtime feedback during a workout may help to solve these issues. Therefore we introduce four fundamental steps for complex human motion assessment and present SensX, a sensor-based architecture for monitoring, recording, and analyzing complex and multi-dimensional motion chains. We provide the results of our preliminary study encompassing 8 different body weight exercises, 20 participants, and more than 9,220 recorded exercise repetitions. Furthermore, insights into SensXs classification capabilities and the impact of specific sensor configurations onto the analysis process are given.Comment: Published within the Proceedings of 14th IEEE International Conference on Networking, Sensing and Control (ICNSC), May 16th-18th, 2017, Calabria Italy 6 pages, 5 figure

Similarity Search for Spatial Trajectories Using Online Lower Bounding DTW and Presorting Strategies

Similarity search with respect to time series has received much attention from research and industry in the last decade. Dynamic time warping is one of the most widely used distance measures in this context. This is due to the simplicity of its definition and the surprising quality of dynamic time warping for time series classification. However, dynamic time warping is not well-behaving with respect to many dimensionality reduction techniques as it does not fulfill the triangle inequality. Additionally, most research on dynamic time warping has been performed with one-dimensional time series or in multivariate cases of varying dimensions. With this paper, we propose three extensions to LB_Rotation for two-dimensional time series (trajectories). We simplify LB_Rotation and adapt it to the online and data streaming case and show how to tune the pruning ratio in similarity search by using presorting strategies based on simple summaries of trajectories. Finally, we provide a thorough valuation of these aspects on a large variety of datasets of spatial trajectories

Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling

State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still construct search trees of considerable size, which could limit the performance due to restricted memory resources. In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to open-loop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four large benchmark problems and compare its performance with different tree-based approaches. We show that POSTS achieves competitive performance compared to tree-based open-loop planning and offers a performance-memory tradeoff, making it suitable for partially observable planning with highly restricted computational and memory resources.Comment: Presented at AAAI 201

The scenario coevolution paradigm: adaptive quality assurance for adaptive systems

Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137–154, 2018)

ВИХОРЕВ Семён Романович

Die vorliegende Arbeit liefert Lösungsansätze für die Laufzeitüberwachung eines Selbst-organisierenden industriellen Systems (SOIS). Dazu wird ein Konzept zur Erkennung und Analyse von Anomalien in raumbezogenen Daten mit Hilfe von Substrukturverzeichnissen vorgestellt. Dieser Ansatz ermöglicht es, den aufgrund der Autonomie des Systems enorm großen Zustandsraum nachzubilden und damit Änderungen im Verhalten einzelner Systemteilnehmer oder auch Änderungen mit globalen Auswirkungen zuverlässig zu detektieren. Im Rahmen dieser Arbeit wurde dieses Konzept zunächst für Bewegungsdaten von Objekten mit konstanter Geschwindigkeit, die sich in SOIS mit Fließbandarchitektur ergeben, umgesetzt und umfänglich mit Hilfe von entsprechenden Simulationsmodellen von SOIS evaluiert. In einem weiteren Schritt wurde die Skalierbarkeit der Substrukturextraktion und die Onlinefähigkeit des Rekonstruktionsprozesses diskutiert. Für beide Fälle konnte eine Modullösung erarbeitet werden, die eine effiziente technische Realisierung des vorgestellten Ansatzes ermöglicht. Schließlich wurde im Rahmen dieser Arbeit die Übertragbarkeit des vorgestellten Konzepts auf weitere, komplexere raumbezogene Daten, die in einem SOIS verarbeitet werden, gezeigt. Dafür wurde das Konzept sowohl für Bewegungsdaten von Objekten mit variabler Geschwindigkeit, als auch für zustandsbeschreibende räumliche Schichtmodelle umgesetzt. Die umfänglichen Evaluierungen der beiden Verfahren bestätigen, dass auch in diesem Fall das vorgestellte Konzept das Problem des enorm großen Zustandraums bei der Erkennung und Analyse von Anomalien in SOIS mit Hilfe von Substrukturverzeichnissen löst. Insgesamt liefert die vorliegende Arbeit damit einen wichtigen Beitrag zum Thema Qualitätssicherung in SOIS, da die vorgestellten Lösungsansätze zur Laufzeitüberwachung als Bestandteil eines Qualitätssicherungsprozesses eingesetzt werden können.This work presents solutions for runtime monitoring in self-organizing industrial systems (SOIS). Therefore, a concept for detecting and analysing anomalies in spatial data by using subpattern dictionaries is presented. This approach allows to model the enormous state space caused by the autonomy of such systems, and, by doing this, to detect reliably both, changes regarding the behaviour of single system components, but also changes of global impact. The presented concept was implemented first for moving data of objects with constant velocity, which can be found in SOIS with conveyors, and evaluated extensively based on suitable simulations of such SOIS. Furthermore, as part of this work, the scalability of the subpattern extraction and the online capability of the reconstruction process is discussed. In both cases modular solutions for an efficient realisation of the presented approach were developed. Finally, the transferability of the presented concept to other, more complex spatial data, which have to be processed in a SOIS, is shown. For doing this, the concept was implemented both, for moving data of objects with variable velocity, but also state describing spatial layer models. The evaluation of both methods confirms that also in this case the presented concept solves the problem of the enormous state spaces when detecting and analysing anomalies in SOIS by using subpattern dictionaries. In summary, this work makes an important contribution for quality assurance in SOIS, as the presented solutions can be used for runtime monitoring as parts of the overall quality assurance process