Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?
Abstract I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research....
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12984-022-01037-z |
| _version_ | 1818211641765920768 |
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| author | Rob Labruyère |
| author_facet | Rob Labruyère |
| author_sort | Rob Labruyère |
| collection | DOAJ |
| description | Abstract I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants. |
| first_indexed | 2024-12-12T05:35:44Z |
| format | Article |
| id | doaj.art-3347402bbfa44bf3ace418799e69be7d |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-12T05:35:44Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-3347402bbfa44bf3ace418799e69be7d2022-12-22T00:36:08ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032022-06-011911510.1186/s12984-022-01037-zRobot-assisted gait training: more randomized controlled trials are needed! Or maybe not?Rob Labruyère0Swiss Children’s Rehab, University Children’s Hospital ZurichAbstract I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants.https://doi.org/10.1186/s12984-022-01037-zLokomatMachine learningDevice settings |
| spellingShingle | Rob Labruyère Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? Journal of NeuroEngineering and Rehabilitation Lokomat Machine learning Device settings |
| title | Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? |
| title_full | Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? |
| title_fullStr | Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? |
| title_full_unstemmed | Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? |
| title_short | Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? |
| title_sort | robot assisted gait training more randomized controlled trials are needed or maybe not |
| topic | Lokomat Machine learning Device settings |
| url | https://doi.org/10.1186/s12984-022-01037-z |
| work_keys_str_mv | AT roblabruyere robotassistedgaittrainingmorerandomizedcontrolledtrialsareneededormaybenot |