Every year thousands of people in Germany suffer severe traumatic brain injuries resulting in a permanent loss of consciousness and the ability to communicate. Our project aims at combining innovative interaction concepts with novel technology to improve the every day life of patients, care takers and relatives. We develop an adaptive, multi-modal training and communication system based on neural activity (EEG) and other sensor data to reinforce the patient's residual reactions and build up basal communication possibilities.
With the help of haptic puzzles, we investigate goal-oriented haptic exploration, search, learning and memory in complex 3D environments in order to both; enable multi-fingered robots with a sense of touch, and gain more insights into human meta-learning.
Twister is a novel haptic interface, consisting of a table with an integrated rotational joint on which a test object can be mounted. Twister measures the angular velocity and the orientation of the mounted object, it also detects when the contact with the object has been established or lost. Twister can provide vibro-tactile feedback. The objects can be rotated either by the study participant or controlled by the integrated motor. Our current research targets the influence of different factors on the performance during haptic rotation. We also pursue application of Twister for hand rehabilitation of stroke patients.
We explore a new BCI design for the control of robotic devices. Specifically, we show the first use of a code-modulating, Visually-Evoked Potential (cVEP)-based BCI for a navigation and control task.
Computer Vision and processing of multimodal sensor data is very important to take Smart Homes to the next level. An intelligent everyday environment should be aware of its residents. It should understand their actions and ideally even be able to predict their behavior. In the KogniHome project, we are developing computer vision and signal processing methods for three demonstrators: (1) KogniChef, a cognitive cooking assistant, (2) KogniMirror, a human aware smart mirror and (3) KogniDoor, an intelligent entrance door.
Every year thousands of people in Germany suffer severe traumatic brain injuries resulting in a permanent loss of consciousness and the ability to communicate. Our project aims at combining innovative interaction concepts with novel technology to improve the every day life of patients, care takers and relatives. We develop an adaptive, multi-modal training and communication system based on neural activity (EEG) and other sensor data to reinforce the patient's residual reactions and build up basal communication possibilities. 
With the help of haptic puzzles, we investigate goal-oriented haptic exploration, search, learning and memory in complex 3D environments in order to both; enable multi-fingered robots with a sense of touch, and gain more insights into human meta-learning.
Twister is a novel haptic interface, consisting of a table with an integrated rotational joint on which a test object can be mounted. Twister measures the angular velocity and the orientation of the mounted object, it also detects when the contact with the object has been established or lost. Twister can provide vibro-tactile feedback. The objects can be rotated either by the study participant or controlled by the integrated motor. Our current research targets the influence of different factors on the performance during haptic rotation. We also pursue application of Twister for hand rehabilitation of stroke patients.
We explore a new BCI design for the control of robotic devices. Specifically, we show the first use of a code-modulating, Visually-Evoked Potential (cVEP)-based BCI for a navigation and control task.
Computer Vision and processing of multimodal sensor data is very important to take Smart Homes to the next level. An intelligent everyday environment should be aware of its residents. It should understand their actions and ideally even be able to predict their behavior. In the