Stephen R. Bested, Valentin A. Crainic, Gerome A. Manson, Luc Tremblay
{"title":"Humans Versus Robots: Converting Golf Putter Trajectories for Robotic Guidance","authors":"Stephen R. Bested, Valentin A. Crainic, Gerome A. Manson, Luc Tremblay","doi":"10.1123/jmld.2022-0031","DOIUrl":null,"url":null,"abstract":"Robotic devices are used to provide physical guidance when teaching different movements. To advance our knowledge of robotic guidance in training complex movements, this investigation tested different kinematic data filtering methods of individual’s golf putts to convert them into trajectories to be employed by a robot arm. The purpose of the current study was to identify a simple filtering method to aptly replicate participants’ individual golf putter trajectories which could be used by the robot to execute them with greater consistency and accuracy than their human counterpart. Participants putted toward three targets where three-dimensional data of the putter’s head were filtered and then fitted by using one or two dimensions of the participant’s putter head trajectories. As expected, both filtering methods employed with the robot outperformed the human participants in ball endpoint accuracy and consistency. Further, after comparing the filtered to the human participants’ trajectories, the two-dimensional method best replicated the kinematic features of human participants’ natural putter trajectory, while the one-dimensional method failed to replicate participant’s backstroke position. This investigation indicates that a two-dimensional filtering method, using Y -forward and Z -vertical position data, can be used to create accurate, consistent, and smooth trajectories delivered by a robot arm.","PeriodicalId":37368,"journal":{"name":"Journal of Motor Learning and Development","volume":"234 1","pages":"0"},"PeriodicalIF":0.8000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Motor Learning and Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1123/jmld.2022-0031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PSYCHOLOGY, DEVELOPMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Robotic devices are used to provide physical guidance when teaching different movements. To advance our knowledge of robotic guidance in training complex movements, this investigation tested different kinematic data filtering methods of individual’s golf putts to convert them into trajectories to be employed by a robot arm. The purpose of the current study was to identify a simple filtering method to aptly replicate participants’ individual golf putter trajectories which could be used by the robot to execute them with greater consistency and accuracy than their human counterpart. Participants putted toward three targets where three-dimensional data of the putter’s head were filtered and then fitted by using one or two dimensions of the participant’s putter head trajectories. As expected, both filtering methods employed with the robot outperformed the human participants in ball endpoint accuracy and consistency. Further, after comparing the filtered to the human participants’ trajectories, the two-dimensional method best replicated the kinematic features of human participants’ natural putter trajectory, while the one-dimensional method failed to replicate participant’s backstroke position. This investigation indicates that a two-dimensional filtering method, using Y -forward and Z -vertical position data, can be used to create accurate, consistent, and smooth trajectories delivered by a robot arm.
期刊介绍:
The Journal of Motor Learning and Development (JMLD) publishes peer-reviewed research that advances the understanding of movement skill acquisition and expression across the lifespan. JMLD aims to provide a platform for theoretical, translational, applied, and innovative research related to factors that influence the learning or re-learning of skills in individuals with various movement-relevant abilities and disabilities.