Abstract
Robot-assisted gait training (RAGT) is a promising tool to improve walking function after stroke and spinal cord injury (SCI), especially when combined with conventional physical therapy. The way how the robot is controlled can have a large influence on active participation of the user and the effectiveness of the training. Previous studies suggest that personalized assistance based on patients' abilities can be beneficial.
In this thesis, we describe four studies about assessment methods and subtask-based gait assistance. In the first study, we developed and evaluated a device that was used to get first estimates of apparent hip joint impedance during walking. In the second study, we described a controller for automatically-tuned (AT) subtask-based assistance and we evaluated the feasibility of using this algorithm as an assessment tool. These first two studies can help in the future to better identify underlying impairments in people with a diminished walking ability. In the third study, we compared AT assistance with manually-tuned assistance and describe differences and similarities. In the fourth study, the effect of changing the assistance for one subtask on other subtasks and spatiotemporal parameters was determined. These last two studies can help to improve assistance tuning in the future.
To sum up, we have taken a next step towards personalized robot-assisted gait training. In the discussion we show that there are still some challenges to overcome in order to apply the optimal robotic gait assistance for each individual. Future research should focus on the long-term effect of various controllers and a better understanding of the exact effect of RAGT on neurorehabilitation after stroke and SCI to further personalize and improve RAGT.
In this thesis, we describe four studies about assessment methods and subtask-based gait assistance. In the first study, we developed and evaluated a device that was used to get first estimates of apparent hip joint impedance during walking. In the second study, we described a controller for automatically-tuned (AT) subtask-based assistance and we evaluated the feasibility of using this algorithm as an assessment tool. These first two studies can help in the future to better identify underlying impairments in people with a diminished walking ability. In the third study, we compared AT assistance with manually-tuned assistance and describe differences and similarities. In the fourth study, the effect of changing the assistance for one subtask on other subtasks and spatiotemporal parameters was determined. These last two studies can help to improve assistance tuning in the future.
To sum up, we have taken a next step towards personalized robot-assisted gait training. In the discussion we show that there are still some challenges to overcome in order to apply the optimal robotic gait assistance for each individual. Future research should focus on the long-term effect of various controllers and a better understanding of the exact effect of RAGT on neurorehabilitation after stroke and SCI to further personalize and improve RAGT.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Award date | 10 Jul 2020 |
| Place of Publication | Enschede |
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| Print ISBNs | 978-90-365-5002-4 |
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| Publication status | Published - 26 Jun 2020 |