Literature DB >> 28983676

The effectiveness of robotic training depends on motor task characteristics.

Laura Marchal-Crespo1,2,3, Nicole Rappo4, Robert Riener4,5.   

Abstract

Previous research suggests that the effectiveness of robotic training depends on the motor task to be learned. However, it is still an open question which specific task's characteristics influence the efficacy of error-modulating training strategies. Motor tasks can be classified based on the time characteristics of the task, in particular the task's duration (discrete vs. continuous). Continuous tasks require movements without distinct beginning or end. Discrete tasks require fast movements that include well-defined postures at the beginning and the end. We developed two games, one that requires a continuous movement-a tracking task-and one that requires discrete movements-a fast reaching task. We conducted an experiment with thirty healthy subjects to evaluate the effectiveness of three error-modulating training strategies-no guidance, error amplification (i.e., repulsive forces proportional to errors) and haptic guidance-on self-reported motivation and learning of the continuous and discrete games. Training with error amplification resulted in better motor learning than haptic guidance, besides the fact that error amplification reduced subjects' interest/enjoyment and perceived competence during training. Only subjects trained with error amplification improved their performance after training the discrete game. In fact, subjects trained without guidance improved the performance in the continuous game significantly more than in the discrete game, probably because the continuous task required greater attentional levels. Error-amplifying training strategies have a great potential to provoke better motor learning in continuous and discrete tasks. However, their long-lasting negative effects on motivation might limit their applicability in intense neurorehabilitation programs.

Keywords:  Continuous task; Discrete task; Error amplification; Feedback motor control; Feedforward motor control; Haptic guidance; Motivation; Motor learning; Rehabilitation robotics; Task characteristics

Mesh:

Year:  2017        PMID: 28983676     DOI: 10.1007/s00221-017-5099-9

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  43 in total

Review 1.  Internal models for motor control and trajectory planning.

Authors:  M Kawato
Journal:  Curr Opin Neurobiol       Date:  1999-12       Impact factor: 6.627

2.  Motor learning elicited by voluntary drive.

Authors:  Martin Lotze; Christoph Braun; Niels Birbaumer; Silke Anders; Leonardo G Cohen
Journal:  Brain       Date:  2003-04       Impact factor: 13.501

3.  Task-specific internal models for kinematic transformations.

Authors:  Christine Tong; J Randall Flanagan
Journal:  J Neurophysiol       Date:  2003-08       Impact factor: 2.714

4.  Endpoint stiffness of the arm is directionally tuned to instability in the environment.

Authors:  David W Franklin; Gary Liaw; Theodore E Milner; Rieko Osu; Etienne Burdet; Mitsuo Kawato
Journal:  J Neurosci       Date:  2007-07-18       Impact factor: 6.167

5.  Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification.

Authors:  Jeremy L Emken; David J Reinkensmeyer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-03       Impact factor: 3.802

6.  Patient-cooperative strategies for robot-aided treadmill training: first experimental results.

Authors:  Robert Riener; Lars Lünenburger; Saso Jezernik; Martin Anderschitz; Gery Colombo; Volker Dietz
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-09       Impact factor: 3.802

7.  Internal models in the cerebellum.

Authors:  D M Wolpert; R C Miall; M Kawato
Journal:  Trends Cogn Sci       Date:  1998-09-01       Impact factor: 20.229

8.  The coordination of arm movements: an experimentally confirmed mathematical model.

Authors:  T Flash; N Hogan
Journal:  J Neurosci       Date:  1985-07       Impact factor: 6.167

9.  A robotic system to train activities of daily living in a virtual environment.

Authors:  Marco Guidali; Alexander Duschau-Wicke; Simon Broggi; Verena Klamroth-Marganska; Tobias Nef; Robert Riener
Journal:  Med Biol Eng Comput       Date:  2011-07-28       Impact factor: 2.602

Review 10.  Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke: a review.

Authors:  Nurdiana Nordin; Sheng Quan Xie; Burkhard Wünsche
Journal:  J Neuroeng Rehabil       Date:  2014-09-12       Impact factor: 4.262

View more
  5 in total

Review 1.  A tale of too many tasks: task fragmentation in motor learning and a call for model task paradigms.

Authors:  Rajiv Ranganathan; Aimee D Tomlinson; Rakshith Lokesh; Tzu-Hsiang Lin; Priya Patel
Journal:  Exp Brain Res       Date:  2020-11-10       Impact factor: 1.972

2.  Neural circuits activated by error amplification and haptic guidance training techniques during performance of a timing-based motor task by healthy individuals.

Authors:  Marie-Hélène Milot; Laura Marchal-Crespo; Louis-David Beaulieu; David J Reinkensmeyer; Steven C Cramer
Journal:  Exp Brain Res       Date:  2018-08-21       Impact factor: 1.972

3.  Haptic Error Modulation Outperforms Visual Error Amplification When Learning a Modified Gait Pattern.

Authors:  Laura Marchal-Crespo; Panagiotis Tsangaridis; David Obwegeser; Serena Maggioni; Robert Riener
Journal:  Front Neurosci       Date:  2019-02-19       Impact factor: 4.677

4.  Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks.

Authors:  Özhan Özen; Karin A Buetler; Laura Marchal-Crespo
Journal:  Front Neurosci       Date:  2021-02-02       Impact factor: 4.677

5.  Cortical reorganization to improve dynamic balance control with error amplification feedback.

Authors:  Yi-Ching Chen; Yi-Ying Tsai; Gwo-Ching Chang; Ing-Shiou Hwang
Journal:  J Neuroeng Rehabil       Date:  2022-01-16       Impact factor: 4.262

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.