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We describe an automated approach, to easily track patients regaining their walking ability while recovering from neurological diseases like e.g. stroke. Based on captured gait data and objective measures derived out of it the rehabilitation process can be optimized and thus steered. In order to apply such system in clinical practice two key requirements have to be fulfilled: (i) the system needs to be applicable in terms of ease of use and performance; (ii) the derived measures need to be accurate.
This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a marker- and contact-less motion capture system that identifies 20 joints of a person walking along a corridor.
Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.
With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable.
Der Einsatz paralleler Hardware-Architekturen betrifft alle Software-Entwickler und -Entwicklerinnen: vom Supercomputer bis zum eingebetteten System werden Multi- und Manycore-Systeme inzwischen eingesetzt. Die Herausforderungen an das Software Engineering sind vielfältig. Zum einen ist (wieder) ein stärkeres Verständnis für die Hardware notwendig. Ohne eine skalierbare Partitionierung der Software und parallele Algorithmen bleibt die Rechenleistung ungenutzt. Zum anderen stehen neue Programmiersprachen im Vordergrund, die die Ausführung von parallelen Anweisungen ermöglichen.
Dieses Buch betrachtet unterschiedliche Aspekte bei der Entwicklung paralleler Systeme und berücksichtigt dabei auch eingebettete Systeme. Es verbindet Theorie und praktische Anwendung und ist somit für Studierende und Anwender in der Praxis gleichermaßen geeignet. Durch die programmiersprachenunabhängige Darstellung der Algorithmen können sie leicht für die eigene Anwendung angepasst werden. Viele praktische Projekte erleichtern das Selbststudium und vertiefen das Gelernte.
Driven by the success of Internet of Things, the number of embedded systems is constantly increasing. Reducing power consumption and improving energy efficiency are among the key challenges for battery-powered embedded systems. Additionally, threats like climate change clearly illustrate the need for systems with low resource usages. Due to the impact of software applications on the system’s power consumption, it is important to optimize the software design even in early development phases. The important role of the software layer is often overlooked because energy consumption is commonly associated with the hardware layer. As a result, existing research mainly focuses on energy optimization at the hardware level, while only limited research has been published on energy optimization at the software design level. This work presents a novel approach to propose an energy-aware software design pattern framework description, which takes power consumption and time behavior into account. We evaluate the expressiveness of the framework by defining design patterns, which use elaborated power-saving strategies for various hardware components to reduce the overall energy consumption of an embedded system. Furthermore, we introduce a dimensionless numerical efficiency factor to make energy savings quantifiable and a comparison for design patterns applied in various use cases possible.
Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning
(2021)
Rapid-Prototyping and Early Validation of Software Models through Uniform Integration of Hardware
(2023)
Due to the resource-constrained nature of embedded systems, it is crucial to support the estimation of their power consumption as early in the development process as possible. Non-functional requirements based on power consumption directly impact the software design, e.g., watt-hour thresholds and expected lifetimes based on battery capacities. Even if software affects hardware behavior directly, these types of requirements are often overlooked by software developers because they are commonly associated with the hardware layer. Modern trends in software engineering such as Model-Driven Development (MDD) can be used in embedded software development to evaluate power consumption-based requirements in early design phases. However, power consumption aspects are currently not sufficiently considered in MDD approaches. In this paper, we present a model-driven approach using Unified Modeling Language profile extensions to model hardware components and their power characteristics. Software m odels are combined with hardware models to achieve a system-wide estimation, including peripheral devices, and to make the power-related impact in early design stages visible. By deriving energy profiles, we provide software developers with valuable feedback, which may be used to identify energy bugs and evaluate power consumption-related requirements. To demonstrate the potential of our approach, we use a sensor node example to evaluate our concept and to identify its energy bugs.