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Robotic Orthosis Lokomat: A Rehabilitation and Research Tool
Abstract
The aim of this article is to introduce the robotic orthosis Lokomat, developed to automate treadmill training rehabilitation of locomotion for spinal cord injured and stroke patients, to the Functional Electrical Stimulation (FES) and Neuromodulation research communities, and to report on our newly conducted research. We first illustrate the primary use of Lokomat in rehabilitation and focus on control aspects and algorithms associated with robotic rehabilitation of locomotion. Then we describe two applications where the Lokomat was used as a research tool. The first application is closed-loop control of the FES-induced shank movement and the second is the development of a neural network model of the spinal locomotor centers. This model was used to derive a neural locomotion controller for the Lokomat.
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“The effectiveness of Lokomat as an intervention in gait rehabilitation to improve overground walking function in neurological patients has been verified through worldwide clinical studies [26].”
From Recent developments and challenges of lower extremity exoskeletons (2016) · cites this paper as [26]
In the citing paper
“In trajectory tracking control, the predefined trajectories of the lower limb joints, usually collected from healthy individuals, are used as the control targets [26].”
From Recent developments and challenges of lower extremity exoskeletons (2016) · cites this paper as [26]
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- Jezernik S, Colombo G, Morari M. Joint-angle trajectory adaptation for the robotic orthosis Lokomat. In: Proceedings of the Workshop on European Scientific and Industrial Collaboration (WESIC). Enschede, The Netherlands, 2001: pp 451–456. (available from http://www.aut.ee.ethz.ch)
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A Pathological Condition Affects Motor Modules in a Bipedal Locomotion Model
Ichimura, D., & Yamazaki, T. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00079 · Cited by 4
Metadata
- Authors
- Jezernik, S., Colombo, G., Keller, T., Frueh, H., & Morari, M.
- Affiliations
- University of Zurich (CH)
- Universitätsklinik Balgrist (CH)
- ETH Zurich (CH)
- Corresponding author
- Jezernik, Sašo
- Venue
- Neuromodulation Technology at the Neural Interface
- Publisher
- Elsevier BV
- Published
- 2003-04
- Volume
- 6
- Issue
- 2
- Pages
- 108-115
- ISSN
- 1094-7159 (ISSN-L)
- OpenAlex
- W2105236479
- Cited by
- 503
- References
- 7
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 21 · 2023: 40 · 2022: 28 · 2021: 46 · 2020: 35 · 2019: 45 · 2018: 32 · 2017: 32 · 2016: 25 · 2015: 29 · 2014: 26 · 2013: 29 · 2012: 14
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuromodulation Technology at the Neural Interface (publishedVersion)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Medicine › Clinical Neurology › Spinal Cord Injury Research
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- Lokomat, robotic orthosis, gait rehabilitation, treadmill training, spinal cord injury, stroke, functional electrical stimulation, feedback control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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