Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders

Research output: Contribution to conferencePosterAcademic

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Abstract

Introduction: In clinical practice, therapists choose the amount of assistance
that patients receive while walking in a robotic gait trainer. A disadvantage is
that therapists cannot directly feel what the device does. Therefore, algorithms
were developed that automatically adjust the assistance, however,
they have not been compared to the settings that therapists would choose.
Main objective: The goal of this study was to compare the assistance set by
an automatically-tuned (AT) algorithm to manually-tuned (MT) assistance in
a robotic gait trainer.

Methods: Ten participants (6x stroke, 4x spinal cord injury) walked with
both approaches in the LOPES II gait trainer. In both cases, the assistance
was adjusted for various subtasks of walking (e.g. step height). Either the
therapist changed the assistance for each subtask (MT) or the AT algorithm
adjusted the assistance based on errors compared to reference trajectories.

Results and discussion: The different approaches did not always focus on
the same subtasks. On average, participants received less assistance with
the AT algorithm for all subtasks. In spite of this, the largest errors
compared to the reference trajectory were found for the MT approach.
A possible reason for this is that therapists might focus on other factors
while setting the assistance.

Conclusion: An automatically-tuned algorithm can decrease deviations
from a reference trajectory, however, large differences were found
compared to the settings chosen by a therapist and further research should
focus on how this information can be used to optimize robotic gait therapy.
Original languageEnglish
Pages214-214
Number of pages1
Publication statusPublished - 23 May 2019
Event3rd Congress on NeuroRehabilitation and Neural Repair 2019 - Maastricht, Netherlands
Duration: 22 May 201924 May 2019
Conference number: 3

Conference

Conference3rd Congress on NeuroRehabilitation and Neural Repair 2019
CountryNetherlands
CityMaastricht
Period22/05/1924/05/19

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Nervous System Diseases
Gait
Robotics
Walking
Spinal Cord Injuries
Stroke
Equipment and Supplies
Research
Therapeutics

Cite this

Fricke, S., Bayon, C., van der Kooij, H., & van Asseldonk, E. (2019). Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders. 214-214. Poster session presented at 3rd Congress on NeuroRehabilitation and Neural Repair 2019, Maastricht, Netherlands.
Fricke, S. ; Bayon, C. ; van der Kooij, H. ; van Asseldonk, E. / Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders. Poster session presented at 3rd Congress on NeuroRehabilitation and Neural Repair 2019, Maastricht, Netherlands.1 p.
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title = "Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders",
abstract = "Introduction: In clinical practice, therapists choose the amount of assistancethat patients receive while walking in a robotic gait trainer. A disadvantage isthat therapists cannot directly feel what the device does. Therefore, algorithmswere developed that automatically adjust the assistance, however,they have not been compared to the settings that therapists would choose.Main objective: The goal of this study was to compare the assistance set byan automatically-tuned (AT) algorithm to manually-tuned (MT) assistance ina robotic gait trainer.Methods: Ten participants (6x stroke, 4x spinal cord injury) walked withboth approaches in the LOPES II gait trainer. In both cases, the assistancewas adjusted for various subtasks of walking (e.g. step height). Either thetherapist changed the assistance for each subtask (MT) or the AT algorithmadjusted the assistance based on errors compared to reference trajectories.Results and discussion: The different approaches did not always focus onthe same subtasks. On average, participants received less assistance withthe AT algorithm for all subtasks. In spite of this, the largest errorscompared to the reference trajectory were found for the MT approach.A possible reason for this is that therapists might focus on other factorswhile setting the assistance.Conclusion: An automatically-tuned algorithm can decrease deviationsfrom a reference trajectory, however, large differences were foundcompared to the settings chosen by a therapist and further research shouldfocus on how this information can be used to optimize robotic gait therapy.",
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Fricke, S, Bayon, C, van der Kooij, H & van Asseldonk, E 2019, 'Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders' 3rd Congress on NeuroRehabilitation and Neural Repair 2019, Maastricht, Netherlands, 22/05/19 - 24/05/19, pp. 214-214.

Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders. / Fricke, S.; Bayon, C.; van der Kooij, H.; van Asseldonk, E.

2019. 214-214 Poster session presented at 3rd Congress on NeuroRehabilitation and Neural Repair 2019, Maastricht, Netherlands.

Research output: Contribution to conferencePosterAcademic

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N2 - Introduction: In clinical practice, therapists choose the amount of assistancethat patients receive while walking in a robotic gait trainer. A disadvantage isthat therapists cannot directly feel what the device does. Therefore, algorithmswere developed that automatically adjust the assistance, however,they have not been compared to the settings that therapists would choose.Main objective: The goal of this study was to compare the assistance set byan automatically-tuned (AT) algorithm to manually-tuned (MT) assistance ina robotic gait trainer.Methods: Ten participants (6x stroke, 4x spinal cord injury) walked withboth approaches in the LOPES II gait trainer. In both cases, the assistancewas adjusted for various subtasks of walking (e.g. step height). Either thetherapist changed the assistance for each subtask (MT) or the AT algorithmadjusted the assistance based on errors compared to reference trajectories.Results and discussion: The different approaches did not always focus onthe same subtasks. On average, participants received less assistance withthe AT algorithm for all subtasks. In spite of this, the largest errorscompared to the reference trajectory were found for the MT approach.A possible reason for this is that therapists might focus on other factorswhile setting the assistance.Conclusion: An automatically-tuned algorithm can decrease deviationsfrom a reference trajectory, however, large differences were foundcompared to the settings chosen by a therapist and further research shouldfocus on how this information can be used to optimize robotic gait therapy.

AB - Introduction: In clinical practice, therapists choose the amount of assistancethat patients receive while walking in a robotic gait trainer. A disadvantage isthat therapists cannot directly feel what the device does. Therefore, algorithmswere developed that automatically adjust the assistance, however,they have not been compared to the settings that therapists would choose.Main objective: The goal of this study was to compare the assistance set byan automatically-tuned (AT) algorithm to manually-tuned (MT) assistance ina robotic gait trainer.Methods: Ten participants (6x stroke, 4x spinal cord injury) walked withboth approaches in the LOPES II gait trainer. In both cases, the assistancewas adjusted for various subtasks of walking (e.g. step height). Either thetherapist changed the assistance for each subtask (MT) or the AT algorithmadjusted the assistance based on errors compared to reference trajectories.Results and discussion: The different approaches did not always focus onthe same subtasks. On average, participants received less assistance withthe AT algorithm for all subtasks. In spite of this, the largest errorscompared to the reference trajectory were found for the MT approach.A possible reason for this is that therapists might focus on other factorswhile setting the assistance.Conclusion: An automatically-tuned algorithm can decrease deviationsfrom a reference trajectory, however, large differences were foundcompared to the settings chosen by a therapist and further research shouldfocus on how this information can be used to optimize robotic gait therapy.

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Fricke S, Bayon C, van der Kooij H, van Asseldonk E. Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders. 2019. Poster session presented at 3rd Congress on NeuroRehabilitation and Neural Repair 2019, Maastricht, Netherlands.