机器人辅助训练的自动反馈选择

N. Gerig, P. Wolf, R. Sigrist, R. Riener, G. Rauter
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引用次数: 2

摘要

摘要机器人辅助训练可以通过在学习过程中使用增强反馈来支持受训者来增强。增强反馈的有效性被认为取决于受训者的技能水平和任务特征。因此,在训练过程中为个体受试者选择最有效的增强反馈是具有挑战性的。我们提出了一个基于预测性能改进的自动反馈选择的通用概念。作为概念的证明,我们将我们的概念应用于trunkarm赛艇。利用现有数据,验证了改进与技能水平相关的假设,并获得了预测线性混合模型。我们使用此模型自动为新学员选择反馈。观察到的改进用于使预测模型适应个体受试者。预测模型没有过度拟合,并通过这种适应将其推广到新的受试者。主要是,在先前的研究中,选择的反馈显示了保留学习的最高基线。通过复制我们以前的最佳结果,我们证明了基于改进预测的简单决策规则有可能合理选择反馈,或向人类主管提供可理解的建议。据我们所知,这是第一次在运动学习中实现自动反馈选择。
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Automated Feedback Selection for Robot-Assisted Training
Abstract Robot-assisted training can be enhanced by using augmented feedback to support trainees during learning. Efficacy of augmented feedback is assumed to be dependent on the trainee's skill level and task characteristics. Thus, selecting the most efficient augmented feedback for individual subjects over the course of training is challenging. We present a general concept to automate feedback selection based on predicted performance improvement. As proof of concept, we applied our concept to trunkarm rowing. Using existing data, the assumption that improvement is skill level dependent was verified and a predictive linear mixed model was obtained. We used this model to automatically select feedback for new trainees. The observed improvements were used to adapt the prediction model to the individual subject. The prediction model did not over-fit and generalized to new subjects with this adaptation. Mainly, feedback was selected that showed the highest baseline to retention learning in previous studies. By this replication of our former best results we demonstrate that a simple decision rule based on improvement prediction has the potential to reasonably select feedback, or to provide a comprehensible suggestion to a human supervisor. To our knowledge, this is the first time an automated feedback selection has been realized in motor learning.
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来源期刊
International Journal of Computer Science in Sport
International Journal of Computer Science in Sport Computer Science-Computer Science (all)
CiteScore
2.20
自引率
0.00%
发文量
4
审稿时长
12 weeks
期刊最新文献
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