Prof. Dr. Ing. Robert Riener

 

Main Goals, Keywords

Rehabilitation engineering, robot-aided neuro-rehabilitation of gait, robot-aided ortho and neuro-rehabilitation of the upper extremities, neurophysiological and biomechanical simulation, motor-learning, Virtual Reality applied to rehabilitation and medical education.

Group Members

1 professor, 1 senior scientist, 1 postdoc, 12 PhD students, 2 technicians, 1 secretary

Previous and Current Research

Patients with spinal cord or brain injuries, orthopaedic lesions, or age-related disorders often require extensive rehabilitation therapies to improve their sensory-motor state and activities of daily living. Rehabilitation is one of the neglected medical fields, where a scientific basis of the applied treatments is often lacking, and even serious evidence for the efficacy of a particular treatment can be absent. Recently, new technologies have become available, which have the potential to closely follow the time course of patient recovery, lessen the burden of physiotherapists, and allow new treatment approaches that have not previously existed. We exploit these new technologies and develop new approaches in order to prove, and improve, the therapeutic efficacy of novel rehabilitative treatments.

Our research focuses on the investigation of the neurological and biomechanical principles of human motor control, motor learning, and movement restoration. We develop multi-modal and user-cooperative techniques to investigate these principles and use them for the rehabilitation of patients with movement disorders. Multi-modal means that the patient is presented with the maximum amount of information by exploiting their visual, acoustic, tactile, and kinaesthetic sensor channels. Cooperative means that the technical system takes into account the patient's effort and intention rather than imposing any rigid and inflexible strategy. Multi-modality and cooperativity have the great potential to improve the therapeutic effects compared to classical rehabilitation strategies.

Our research resulted in different robotic movement rehabilitation devices and systems for the upper and lower extremities as well as the trunk. The systems are being technically and clinically evaluated in cooperation with our medical partners. Patient groups being involved are mainly paraplegics, tetraplegics, hemiplegics, and patients with shoulder pathologies. In addition, we are developing advanced man-machine interfaces and Virtual Reality technologies for rehabilitation-related applications.

Future Projects

We will continue our efforts to make the therapies also applicable to other patient groups such as Multiple Sclerosis, and Cerebral Palsy patients. A special focus will be the implementation of multi-modal (i.e., graphical, acoustic, haptic) training methods in order to study motor learning and motor rehabilitation in man.

Techniques and Equipment

Several devices are available at the University Hospital Balgrist that can be used to analyze and synthesize human movements, such as the driven gait orthosis Lokomat®, different body-weight support systems, the arm therapy robot ARMin, several treadmills, the optical gait analysis system Vicon, EMG recording equipment, sensor equipment (goniometers, pressure insoles, acceleration sensors, force plate), functional electrical stimulation (FES) equipment etc.
At ETH Zurich we have excellent computing facilities and access to all relevant simulation and control design software. Furthermore, there is an electronic laboratory and access to the machine shop of the department. Available devices are a Multi-Moment-Chair to measure 7 degree-of-freedom isometric leg joint moments, haptic display devices, as well as force and position sensors.

Selected Publications

• Novak D, Omlin X, Leins R, Riener R. Predicting targets of human reaching motions using different sensing technologies. IEEE Trans Biomed Eng. 2013 May 13. [Epub ahead of print]
• Riener R, Dislaki E, Keller U, Koenig A, Van Hedel H, Nagle A. Virtual reality aided training of combined arm and leg movements of children with CP. Stud Health Technol Inform. 2013;184:349-55.
• Marchal-Crespo L, Zimmermann R, Lambercy O, Edelmann J, Fluet MC, Wolf M, Gassert R, Riener R. Motor execution detection based on autonomic nervous system responses. Physiol Meas. 2013 Jan;34(1):35-51. doi: 10.1088/0967-3334/34/1/35. Epub 2012 Dec 18.
• Pennycott A, Wyss D, Vallery H, Klamroth-Marganska V, Riener R. Towards more effective robotic gait training for stroke rehabilitation: a review. J Neuroeng Rehabil. 2012 Sep 7;9:65. doi: 10.1186/1743-0003-9-65. Review.

Selected Lectures, Seminars or Colloquia

• Virtual Reality in medicine
• Rehabilitation engineering
• Technical kinesiology

Funding

Swiss National Science Foundation, EU, companies and private foundations

URL

www.sms.mavt.ethz.ch/