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Cited by 1,182Stroke Rehabilitation and RecoveryProsthetics and Rehabilitation RoboticsMotor Control and Adaptation

Review of control strategies for robotic movement training after neurologic injury

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Marchal-Crespo, Laura, Reinkensmeyer, David J·2009·Journal of NeuroEngineering and Rehabilitation
DOI 10.1186/1743-0003-6-20PMC PMC2710333
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Abstract

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.

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  • Temporarily separating haptic guidance and task forces might be important for reducing the user's reliance on feedback.

    “Temporarily separating haptic guidance and task forces, to the contrary, might be important for reducing the user's reliance on the feedback (Li, Patoglu, & O'Malley, 2009;Marchal-Crespo & Reinkensmeyer, 2009).”

    From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2009

    The Assistive controllers section describes fading robotic guidance, and the Performance-based adaptation section says reducing assistance prevented participants from relying on it (page numbers unavailable). The paper does not describe temporarily separating haptic guidance from task forces, so it supports the rationale but not the proposed method. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Haptic guidance may lead to muscle strengthening, somatosensory brain plasticity, reinforcement of movement patterns, therapist relief, and increased motivation.

    “Haptic guidance may lead to (1) strengthening of muscles and connective tissue, provoking motor plasticity and preventing stiffening; (2) somatosensory stimulation inducing brain plasticity; (3) reinforcement of the movement pattern by movement repetitions; (4) prolonged trainings by relieving therapists from back-breaking work; and (5) increased motivation due to successful active task completion”

    From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2009

    The Assistive controllers section (page unavailable) presents somatosensory stimulation, reinforcement through repetition, and motivation from successful movement as possible rationales for assistance. It links effort to motor plasticity and stretching to preventing stiffness, but does not say that guidance strengthens muscles and connective tissue. The passages found do not support the claim that guidance prolongs training by relieving therapists of physically demanding work. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Reliance on haptic guidance might be reduced using assist-as-needed or fading feedback.

    “Reliance on haptic guidance might be reduced using paradigms like assist-as-needed or fading feedback (Marchal-Crespo & Reinkensmeyer, 2009;Patoglu, Li, & O'Malley, 2009;Powell & O'Malley, 2012).”

    From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2009

  • Using position control as haptic augmented feedback may still be useful in rehabilitation, for example for mobilization with high repetitions and reestablishing normative motor output.

    “However, using position control as a haptic augmented feedback strategy may still be of use in rehabilitation-for example, through mobilization with a high number of repetitions and to reestablish normative patterns of motor output (Marchal-Crespo & Reinkensmeyer, 2009).”

    From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Marchal-Crespo & Reinkensmeyer, 2009

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Cited by

Sorted by citations

Authors
Marchal-Crespo, L., & Reinkensmeyer, D. J.
Affiliations
University of California, Irvine (US)
ORCID
Reinkensmeyer, David J
Venue
Journal of NeuroEngineering and Rehabilitation
Publisher
Springer Science and Business Media LLC
Published
2009-06-16
Volume
6
Issue
1
Pages
20-20
ISSN
1743-0003 (ISSN-L)
DOI
10.1186/1743-0003-6-20
PMC
PMC2710333
OpenAlex
W2164553222
Cited by
1,182
References
10
Citations by year
2026: 75 · 2025: 61 · 2024: 49 · 2023: 59 · 2022: 61 · 2021: 74 · 2020: 70 · 2019: 84 · 2018: 106 · 2017: 90 · 2016: 86 · 2015: 71 · 2014: 95 · 2013: 76 · 2012: 50
FWCI
5.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Open Access CRIS of the University of Bern
Funders
  • National Institutes of Health (N01-HD-3–)
  • National Center for Research Resources (M-01RR00827, M01RR000827)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (N01 HD 33352)
Topics
  • Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Keywords
robot-assisted therapy, neurologic injury, stroke rehabilitation, spinal cord injury, impedance control, EMG-based control, adaptive control, haptic simulation, motor recovery, control strategy comparison
MeSH terms
Algorithms, Electromyography, Humans, Robotics, Trauma, Nervous System, Musculoskeletal Manipulations

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