TY  - THES
U1  - Bachelor Thesis
A1  - Seele, Sven
T1  - Gesture control in virtual environments
N2  - The introduction of gestures as a supplementary input modality has become of increasing interest to human computer interaction design, especially for 3D computer environments. This thesis describes the concepts and development of a gesture recognition system based on the machine learning technique of Hidden Markov Models. Well-known from the field of speech recognition, this statistical method is employed in this thesis to represent and recognize predefined gestures. Within this work, gestures are defined as symbols, such as simple geometric shapes or Roman letters. They are extracted from a stream of three-dimensional optical tracking data which is resampled, reduced to 2D and quantized to be used as input to discrete Hidden Markov Models. A set of prerecorded training data is used to learn the parameters of the models and recognition is achieved by evaluating the trained models. The devised system was used to augment an existing virtual reality prototype application which serves as a demonstration and development platform for the VRGeo consortium. The consortium's goal is to investigate and utilize the benefits of virtual reality technology for the oil and gas industry. An isolated test of the system with seven gestures showed accuracies of up to 98.57% and the review from experts in the fields of virtual reality and geophysics was predominantly positive.
U6  - https://doi.org/10.24406/publica-fhg-277842
DO  - https://doi.org/10.24406/publica-fhg-277842
SP  - X, 67
S1  - X, 67
PB  - Fraunhofer Publica
ER  -