Error fields: personalized robotic movement training that augments one’s more likely mistakes
Abstract Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person’s individual reac...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-87331-x |
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| author | Naveed Reza Aghamohammadi Moria Fisher Bittmann Verena Klamroth-Marganska Robert Riener Felix C. Huang James L. Patton |
| author_facet | Naveed Reza Aghamohammadi Moria Fisher Bittmann Verena Klamroth-Marganska Robert Riener Felix C. Huang James L. Patton |
| author_sort | Naveed Reza Aghamohammadi |
| collection | DOAJ |
| description | Abstract Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person’s individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better. This study reports results from a clinical trial registered on ClinicalTrials.gov with ID: NCT02720341. |
| format | Article |
| id | doaj-art-19ebb86e3d734d9cb61a62e1071a92c2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-19ebb86e3d734d9cb61a62e1071a92c22025-02-09T12:33:54ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-87331-xError fields: personalized robotic movement training that augments one’s more likely mistakesNaveed Reza Aghamohammadi0Moria Fisher Bittmann1Verena Klamroth-Marganska2Robert Riener3Felix C. Huang4James L. Patton5Robotics Laboratory, Center for Neural Plasticity, Shirley Ryan AbilityLabRobotics Laboratory, Center for Neural Plasticity, Shirley Ryan AbilityLabKlinik Adelheid, Rehabilitation CentreDepartment of Health Sciences and Technology, Swiss Federal Institute of TechnologyDepartment of Mechanical Engineering, Tufts UniversityRobotics Laboratory, Center for Neural Plasticity, Shirley Ryan AbilityLabAbstract Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person’s individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better. This study reports results from a clinical trial registered on ClinicalTrials.gov with ID: NCT02720341.https://doi.org/10.1038/s41598-025-87331-x |
| spellingShingle | Naveed Reza Aghamohammadi Moria Fisher Bittmann Verena Klamroth-Marganska Robert Riener Felix C. Huang James L. Patton Error fields: personalized robotic movement training that augments one’s more likely mistakes Scientific Reports |
| title | Error fields: personalized robotic movement training that augments one’s more likely mistakes |
| title_full | Error fields: personalized robotic movement training that augments one’s more likely mistakes |
| title_fullStr | Error fields: personalized robotic movement training that augments one’s more likely mistakes |
| title_full_unstemmed | Error fields: personalized robotic movement training that augments one’s more likely mistakes |
| title_short | Error fields: personalized robotic movement training that augments one’s more likely mistakes |
| title_sort | error fields personalized robotic movement training that augments one s more likely mistakes |
| url | https://doi.org/10.1038/s41598-025-87331-x |
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