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Background: The development of mobile interventions for noncommunicable diseases has increased in recent years. However, there is a dearth of apps for patients with peripheral arterial disease (PAD), who frequently have an impaired ability to walk.
Objective: Using a patient-centered approach for the development of mobile interventions, we aim to describe the needs and requirements of patients with PAD regarding the overall care situation and the use of mobile interventions to perform supervised exercise therapy (SET).
Methods: A questionnaire survey was conducted in addition to a clinical examination at the vascular outpatient clinic of the West-German Heart and Vascular Center of the University Clinic Essen in Germany. Patients with diagnosed PAD were asked to answer questions on sociodemographic characteristics, PAD-related need for support, satisfaction with their health care situation, smartphone and app use, and requirements for the design of mobile interventions to support SET.
Results: Overall, a need for better support of patients with diagnosed PAD was identified. In total, 59.2% (n=180) expressed their desire for more support for their disease. Patients (n=304) had a mean age of 67 years and half of them (n=157, 51.6%) were smartphone users. We noted an interest in smartphone-supported SET, even for people who did not currently use a smartphone. “Information,” “feedback,” “choosing goals,” and “interaction with physicians and therapists” were rated the most relevant components of a potential app.
Conclusions: A need for the support of patients with PAD was determined. This was particularly evident with regard to disease literacy and the performance of SET. Based on a detailed description of patient characteristics, proposals for the design of mobile interventions adapted to the needs and requirements of patients can be derived.
The classification of waste with neural networks is already a topic in some scientific papers. An application in the embedded systems area with current AI processors to accelerate the inference has not yet been discussed. In this master work a prototype is created which classifies waste objects and automatically opens the appropriate container for the object. The area of application is in the public space.
For the classification a dataset with 25,681 images and 11 classes is created to re-train the Convolution Neuronal Networks EfficientNet-B0, MobileNet-v2 and NASNet-mobile. These Convolution Neuronal Networks run on the current Edge \acrshort{ai} processors from Google, Intel and Nvidia and are compared for performance, consumption and accuracy.
The master thesis evaluates the result of these comparisons and shows the advantages and disadvantages of the respective processors and the CNNs. For the prototype, the most suitable combination of hardware and AI architecture is used and exhibited at the university fair KasetFair2020. An opinion survey on the application of the machine is conducted.
Integration of an industrial robot manipulator in ROS to enhance its spatial perception capabilities
(2020)
Robots without any external sensors are not capable of sensing their environment, often leading to damaging collisions. These collisions could potentially be avoided if the robot had a way to sense its environment in the first place. This thesis attempts to tackle this problem by equipping such a robot with extra sensor hardware for perceiving environmental objects. The robot used within this thesis is a KUKA LBR iiwa 7 R800. The goal is a robot capable of moving in an unseen environment without colliding with obstacles nearby.
The research covers different sensor options, robots in cramped areas as well as algorithms and simulation topics. Software platforms and libraries used for the implementation are briefly introduced.
Multiple infrared sensors are directly installed onto the robot manipulator. The extra sensors and the robot are integrated into the ROS middleware to create an application capable of sensing the robots’ environment and plan collision-free paths accordingly.
The experiments show, that the low amount of available sensor data can not map the robots’ environment with enough detail. Additional problems, such as sensor noise corrupting parts of the generated map or the robot recognizing itself as an obstacle, lead to a negative result in total. In future work, the choice of sensors shall be reconsidered and tested upfront via simulation software.
An implementation approach of the gap navigation tree using the TurtleBot 3 Burger and ROS Kinetic
(2020)
The creation of a spatial model of the environment is an important task to allow the planning of routes through the environment. Depending on the number of sensor inputs different ways of creating a spatial environment model are possible. This thesis introduces an implementation approach of the Gap Navigation Tree which is aimed for usage with robots that have a limited amount of sensors. The Gap Navigation Tree is a tree structure based on depth discontinuities constructed from the data of a laser scanner. Using the simulated TurtleBot 3 Burger and ROS kinetic a framework is created that implements the theory of the Gap Navigation Tree. The framework is structured in a way that allows using different robots with different sensor types by separating the detection of depth discontinuities from the building and updating of the Gap Navigation Tree.