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Open Access Research

Controlling patient participation during robot-assisted gait training

Alexander Koenig12*, Ximena Omlin12, Jeannine Bergmann4, Lukas Zimmerli13, Marc Bolliger2, Friedemann Müller4 and Robert Riener12

Author Affiliations

1 Sensory-Motor Systems Lab, Department of Mechanical Engineering and Process Engineering, ETH Zurich, Switzerland

2 Spinal Cord Injury Center, Balgrist University Hospital, University Zurich, Switzerland

3 Hocoma AG., Volketswil, Switzerland

4 Schön Klinik Bad Aibling, Germany

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Journal of NeuroEngineering and Rehabilitation 2011, 8:14  doi:10.1186/1743-0003-8-14

Published: 23 March 2011

Abstract

Background

The overall goal of this paper was to investigate approaches to controlling active participation in stroke patients during robot-assisted gait therapy. Although active physical participation during gait rehabilitation after stroke was shown to improve therapy outcome, some patients can behave passively during rehabilitation, not maximally benefiting from the gait training. Up to now, there has not been an effective method for forcing patient activity to the desired level that would most benefit stroke patients with a broad variety of cognitive and biomechanical impairments.

Methods

Patient activity was quantified in two ways: by heart rate (HR), a physiological parameter that reflected physical effort during body weight supported treadmill training, and by a weighted sum of the interaction torques (WIT) between robot and patient, recorded from hip and knee joints of both legs. We recorded data in three experiments, each with five stroke patients, and controlled HR and WIT to a desired temporal profile. Depending on the patient's cognitive capabilities, two different approaches were taken: either by allowing voluntary patient effort via visual instructions or by forcing the patient to vary physical effort by adapting the treadmill speed.

Results

We successfully controlled patient activity quantified by WIT and by HR to a desired level. The setup was thereby individually adaptable to the specific cognitive and biomechanical needs of each patient.

Conclusion

Based on the three successful approaches to controlling patient participation, we propose a metric which enables clinicians to select the best strategy for each patient, according to the patient's physical and cognitive capabilities. Our framework will enable therapists to challenge the patient to more activity by automatically controlling the patient effort to a desired level. We expect that the increase in activity will lead to improved rehabilitation outcome.