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Head Mounted Displays (HMD) are increasingly used in various industries. But apart from the industry environment, the potentials of HMDs in a private environment like at home has been rel- atively unexplored so far. What daily tasks can these help with, in the home kitchen for example?
The aim of this thesis is to obtain knowledge about the usefulness of such an HMD, the HoloLens, in combination with an application, while following a new recipe. Therefore a prototype applica- tion for the HoloLens got developed which guides a user through the cooking of a sushi burger by using multimedia content.
With a mixed method design, consisting of quantitative and qualitative methods, the HoloLens in combination with an application was evaluated by 14 participants.
Not only the weight of the device was a problem for users. The test also revealed that the display is darkening the view and participants tend to look below the glasses. An advantage is indeed to reach the next cooking step without the need of using hands and always having in sight what needs to be done next. Positive feedback was given as well for the application. Through voice control the user communicates to a character which will guide through the recipe by videos and text.
If in future the technical characteristics of HMD devices will improve, an application in this con- text will be of advantage in order to simplify learning a new recipe. This device, in combination with an application, could help early-middle stage cognitive impaired people and blind people to cook.
Deep learning methods have proven highly effective for object recognition tasks, especially
in the form of artificial neural networks. In this bachelor’s thesis, a way is shown to imple-
ment a ready-to-use object recognition implementation on the NAO robotic platform using
Convolutional Neural Networks based on pretrained models. Recognition of multiple objects
at once is realized with the help of the Multibox algorithm. The implementation’s object
recognition rates are evaluated and analyzed in several tests.
Furthermore, the implementation offers a graphical user interface with several options to
adjust the recognition process and for controlling movements of the robot’s head in order
to easier acquire objects in the field of view. Additionally, a dialogue system for querying
further results is presented.