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In this work we present a wearable input device which enables the user to input text into a computer. The text is written into the air via character gestures, like using an imaginary blackboard. To allow hands-free operation, we designed and implemented a data glove, equipped with three gyroscopes and three accelerometers to measure hand motion. Data is(More)
— Human motion recognition is traditionally approached by either recognizing basic motions from features derived from video input or by interpreting complex motions by applying a high-level hierarchy of motion primitives. The former method is usually limited to rather simple motions while the latter requires human expert knowledge to build up a suitable(More)
The fast and robust recognition of human actions is an important aspect for many video-based applications in the field of human computer interaction and surveillance. Although current recognition algorithms provide more and more advanced results, their usability for on-line applications is still limited. To bridge this gap a on-line video-based action(More)
— In this paper, a multi-level approach to intention, activity, and motion recognition for a humanoid robot is proposed. Our system processes images from a monocular camera and combines this information with domain knowledge. The recognition works on-line and in real-time, it is independent of the test person, but limited to predefined viewpoints. Main(More)
We introduce BioKIT, a new Hidden Markov Model based toolkit to preprocess, model and interpret biosignals such as speech, motion, muscle and brain activities. The focus of this toolkit is to enable researchers from various communities to pursue their experiments and integrate real-time biosignal interpretation into their applications. BioKIT boosts a(More)
In robotics research is an increasing need for knowledge about human motions. However humans tend to perceive motion in terms of discrete motion primitives. Most systems use data-driven motion seg-mentation to retrieve motion primitives. Besides that the actual intention and context of the motion is not taken into account. In our work we propose a procedure(More)
Gaussian mixture models are the most popular probability density used in automatic speech recognition. During decoding, often many Gaussians are evaluated. Only a small number of Gaussians contributes significantly to probability. Several promising methods to select relevant Gaussians are known. These methods have different properties in terms of required(More)
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