Learning a locomotor task: with or without errors?

In relearning how to walk you have to get close to falling so you can recognize the signs and correct them. Scare your PT, Demand to  practice fall prevention by forcing stumbles and recovering from them. Throw away the gait belt, that sense of security is preventing you from learning how to walk again properly.
http://www.jneuroengrehab.com/content/11/1/25

Laura Marchal–Crespo^14*, Jasmin Schneider², Lukas Jaeger¹³⁴ and Robert Riener¹⁴

• * Corresponding author: Laura Marchal–Crespo laura.marchal@hest.ethz.ch

Author Affiliations

¹ Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems IRIS, ETH Zurich, Zurich, Switzerland
² Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
³ Clinic for Neuroradiology, University Hospital Zurich, Zurich, Switzerland
⁴ Medical Faculty, Balgrist University Hospital, University of Zurich, Zurich, Switzerland

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Journal of NeuroEngineering and Rehabilitation 2014, 11:25  doi:10.1186/1743-0003-11-25

The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/11/1/25

Received:	1 February 2013
Accepted:	8 February 2014
Published:	4 March 2014

© 2014 Marchal-Crespo et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task.

Methods

In this study, the impact of robotic training strategies that amplify or reduce errors on muscle activation and motor learning of a simple locomotor task was investigated in twenty two healthy subjects. The experiment was conducted with the MAgnetic Resonance COmpatible Stepper (MARCOS) a special robotic device developed for investigations in the MR scanner. The robot moved the dominant leg passively and the subject was requested to actively synchronize the non-dominant leg to achieve an alternating stepping-like movement. Learning with four different training strategies that reduce or amplify errors was evaluated: (i) Haptic guidance: errors were eliminated by passively moving the limbs, (ii) No guidance: no robot disturbances were presented, (iii) Error amplification: existing errors were amplified with repulsive forces, (iv) Noise disturbance: errors were evoked intentionally with a randomly-varying force disturbance on top of the no guidance strategy. Additionally, the activation of four lower limb muscles was measured by the means of surface electromyography (EMG).

Results

Strategies that reduce or do not amplify errors limit muscle activation during training and result in poor learning gains. Adding random disturbing forces during training seems to increase attention, and therefore improve motor learning. Error amplification seems to be the most suitable strategy for initially less skilled subjects, perhaps because subjects could better detect their errors and correct them.

Conclusions

Error strategies have a great potential to evoke higher muscle activation and provoke better motor learning of simple tasks. Neuroimaging evaluation of brain regions involved in learning can provide valuable information on observed behavioral outcomes related to learning processes. The impacts of these strategies on neurological patients need further investigations. 

Figure 1 pictures here; looks like supine walking.