Research Overview

Our research interests are centered around the computational aspects of Human-Computer-Interaction (HCI). As technologies shift computing away from the desktop, it is becoming increasingly clear that traditional means of interaction need to be replaced or complemented with new forms of input and output. The main objective is to push the boundaries of what humans can do with computers and how they interact with them.

Concretely, we research machine perception of human activity, including pose estimation, gesture recognition and other forms of input sensing in order to build models of human behavior. Such models can then be used to provide context sensitive and intelligent feedback and to optimize presentation of information.

Input Recognition

Gestural Interfaces
Motion Analysis
Pose Estimation
Tracking & Detection

Computational Design

Device Synthesis
UI Optimization
Digital Fabrication

Machine Learning

Preference Learning
User Modelling
Deep Learning
Policy Learning

Robotics
AR
VR
Mobile

Research Areas

Specifically, we are interested in algorithms that can extract high-level concepts such as style and semantic meaning from observations of human activities and algorithms that continuously update individualized user models (based on such data). Finally, we investigate how mathematical optimization techniques can be used to synthesize and optimize user interfaces, at design- and run-time. For example, finding optimal parametrizations of sensor-based interactive devices for a given input task or finding optimized ways to display information given the systems current best believe about the user's intention, attention and other states.

We apply this algorithmic work in many different domains such as Augmented and Virtual Reality, Human Robot Interaction and Smart Environments and more.

Input Recognition & Motion Analysis

Gesture recognition
Input sensing
Random Forests
Dataset
 
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Wearable interaction
Input sensing
Image processing
Hand pose estimation
 
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Dynamic gesture recognition
Machine learning
 
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Input devices
Tangible UIs
Computational design
Digital fabrication
 
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Machine Learning for Interaction

Dynamic gesture recognition
Deep Learning
RNNs
RF sensing
 
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Action recognition
Deep Learning
Multi-modal
Computer vision
 
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Gesture recognition
Input sensing
Random Forests
Mobile interaction
 
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Computational Design of Interactive Technologies

Input devices
Tangible UIs
Computational design
Digital fabrication
 
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Input devices
Tangible UIs
Computational design
Digital fabrication
 
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Aerial Robotics
Optimization-based planning
Computational design
Computer assisted design
 
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Robotics
Computational design
Digital fabrication
 
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Application Domains


Page under (re-)construction

This page will be extended soon. In the meantime also check out our publications page for an overview of our research activities.


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