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Consortium: Prof. Dr. Ioannis Iossifidis (PI, consortium lead), Dr. Christian Klaes (PI), Ruhr University Bochum-Knappschaft University Hospital, Prof. Dr. Martin Tegenthoff (PI) Ruhr University- Bergmannsheil University Hospital, Dr. Corinna Weber, Snap GmbH (PI))

• Project duration: 01/2020 — 12/2022
• Funding volume: € 2.092.917,-

Restrictions in hand and arm function are a highly relevant consequence of neurological diseases, such as Parkinson’s disease, strokes or spinal cord injuries, and have an enormous impact on the quality of life and participation of affected patients. A differentiated diagnosis of hand and arm function is of great importance both for therapy control and for early detection. At present, however, very different and only conditionally objectifiable test instruments are used for specific indications, which result in an inaccuracy retest and make it difficult to compare results. In addition, relevant biosignals such as EEG and EMG are not used or not used in direct combination with the functional tests. 

In this project a standardized test environment in Virtual Reality (VR) will be developed. Motion trackers and VR gloves cover a broad spectrum of relevant motion parameters. In particular, synchronous electroencephalographic (EEG) recordings supplement the motion data with neuronal signals. This combination makes it possible for the first time to use modern Machine Learning (ML) algorithms, such as deep learning, in the context of diagnosis and therapy of neurological diseases with hand and arm dysfunctions. 

The easy-to-use functional test can be used in particular for complex extrapyramidal and/or cerebellar movement disorders in order to objectively classify the movement deficits and compare them with comparative data. Due to the increased sensitivity of the test and a generative model of arm movements, different stages of the disease can be better distinguished and the course of the disease more clearly documented. The early detection of diseases with a gradual course should also be improved. Therapeutically, the insights gained in tremor treatment – here by means of a hand exoskeleton to be developed in the project – will be used to establish VR-supported neurofeedback therapy approaches for extrapyramidal and cerebellar movement disorders and for fine calibration for deep brain stimulation for Parkinson’s treatment. 

Movement and correlated EEG data from both healthy volunteers and patients will be used to build a publicly accessible database. The database will be made available as a reference for the development and validation of models and methods for the scientific community and companies. The developed modular hardware and software components will be used clinically / scientifically (VR test environment) as well as economically (test instrument, decoder, hand exoskeleton).

Treffen Sie uns auf der ITSC2012 in Alaska. Wir präsentieren unsere Arbeit zur Entwicklung einer integrierten Architektur zur Entwicklung und Prüfung von Fahrerassistenzsystemen.

Abastract: Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described.
This makes the development of a new methodology and especially the development of an experimental environment necessary, reflecting all constraints of the task to be observed, incorporating the influence of each single component and the generation of variations over scenes and behaviours. In this contribution we focus on the development of an integrated architecture based on a simulated reality framework incorporating simple behavioural models of autonomous acting humans, physical plausible behaving scene objects, a dynamic scene generator and an advanced recording and analysing system, operating on-line on all data streams. The architecture allows to develop, asses and benchmark embedded components, as well as whole ADAS.

Meet us at the ITSC2012 in Alaska. We will present our work about an integrated architecture for the development and assessment of ADAS.

Abastract: Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described.

This makes the development of a new methodology and especially the development of an experimental environment necessary, reflecting all constraints of the task to be observed, incorporating the influence of each single component and the generation of variations over scenes and behaviours. In this contribution we focus on the development of an integrated architecture based on a simulated reality framework incorporating simple behavioural models of autonomous acting humans, physical plausible behaving scene objects, a dynamic scene generator and an advanced recording and analysing system, operating on-line on all data streams. The architecture allows to develop, asses and benchmark embedded components, as well as whole ADAS.

The generation of behavior for autonomous robots in complex environments is restricted by a potentially large number of constraints that must be observed. For many tasks, the relevant constraints can be expressed in a specific, low-dimensional reference frame. The dynamic systems approach to autonomous robotics represents constraints as attractors or repellors over such behaviorally relevant variables. This article presents a framework in which each constraint is treated independently as a dynamical system over a behavioral variable and provides a mechanism for the integration of these dynamical systems as contributions to a common vector field for movement generation. As a demonstration, we treat manipulation tasks for long cylindrical objects as a collection of separate constraints that are interpreted and solved with the proposed techniques. The resulting system is implemented on the small humanoid robot Nao and used to solve two exemplary movement tasks.

Student project within the module “Autonomous Systems” at Ruhr West University of Applied Sciences.

Student project within the module “Autonomous Systems” at Ruhr West University of Applied Sciences.

Student project within the module “Autonomous Systems” at Ruhr West University of Applied Sciences.

The goal of this project is to develop a gesture recognition based on common webcams and low budget hardware to implement hand gesture control in any application.

Student project within the module “Autonomous Systems” at Ruhr West University of Applied Sciences.

The project aim to create a dialog bot with an animated avatar able to have a casual conversation and to assist the user. The dialog bot is supposed to enrich the social live especially of elders people.