[vc_row][vc_column width=”1/4″][vc_single_image image=”8108″ css=”.vc_custom_1464177786646{margin-top: 10px !important;}”][/vc_column][vc_column width=”1/2″][vc_column_text]Contact person: Dr.-Ing. Norbert Schmitz
Funding by: EU
[/vc_column_text][/vc_column][vc_column width=”1/4″][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Augmented and virtual reality are becoming more and more common in systems for user assistance, educational simulators, novel games, and the whole range of applications in between. Technology to automatically capture, recognize, and render human activities is an essential part for all these applications. The aim of COGNITO is to bring this technology a big step forward.


Cognitive Workflow Capturing and Rendering with On-Body Sensor-Networks


The automatic capture, recognition and rendering of human sensory-motor activities represent essential technologies in many diverse applications, ranging from 3D virtual manuals through to training simulators and novel computer games. Although capture systems already exist on the market, they focus primarily on capturing raw motion data, matched to a coarse model of the human body. Moreover, the recorded data is organised as a single cinematic sequence, with little or no reference to the underlying task activity or workflow patterns exhibited by the human subject. The result is data that is difficult to use in all but the most straightforward of applications, requiring extensive editing and user manipulation, especially when cognitive understanding of human action is a key concern, such as in virtual manuals or training simulators.
The aim of the COGNITO project is to address these issues by advancing both the scope and the capability of human activity capture, recognition and rendering. Specifically, we propose to develop novel techniques that will allow cognitive workflow patterns to be analysed, learnt, recorded and subsequently rendered in a user-adaptive manner. Our concern will be to map and closely couple both the afferent and efferent channels of the human subject, enabling activity data to be linked directly to workflow patterns and task completion. We will focus particularly on tasks involving the hand manipulation of objects and tools due to their importance in many industrial applications.
The key objectives of the project are to develop a novel on-body sensor network consisting of miniature inertial and vision sensors, estimate an osteo-articular model of the human body, recover the workflow digitally, and develop novel rendering mechanism for effective and user-adaptive visualization. The work will done within the context of designing effective user assistance systems based around Augmented Reality techniques for specialised industrial manufacture and will be carried out in close collaboration with industrial and end user partners.
Project homepage: