Passenberg Carolina, Glaser Antonia, Peer Angelika
IEEE Trans Haptics. 2013 Oct-Dec;6(4):440-52. doi: 10.1109/TOH.2013.34.
Haptic assistants augment user commands to facilitate manipulation and to increase task performance. The strength of assistance, also referred to as assistance level, is one of the main design factors. While existing implementations mainly realize fixed assistance levels that are selected with respect to one design objective, we introduce an assistance policy module that dynamically changes assistance levels and can incorporate multiple performance measures. The design space of this assistance policy module is systematically analyzed and three design factors, 1) performance criteria, 2) performance reference, and 3) assistance policy, are identified. Different implementations of the assistance policy module are compared for a scenario involving guiding virtual fixtures. A single-user evaluation is used to illustrate the effect of the different implementations on the determined assistance levels, and a multi-user study allows for a statistical comparison of them. Results show that adaptive assistance policies can outperform constant assistance policies, switching assistance policies have advantages over continuously adapting policies, a multi-criteria performance measure should be favored if there is no single criterion that has an outstanding priority, and the selection of the performance reference is highly application dependent.
触觉辅助工具增强用户指令，以方便操作并提高任务执行能力。辅助强度，也称为辅助级别，是主要设计因素之一。虽然现有实现主要实现针对一个设计目标选择的固定辅助级别，但我们引入了一个辅助策略模块，该模块可动态更改辅助级别，并可纳入多种性能指标。对该辅助策略模块的设计空间进行了系统分析，并确定了三个设计因素：1）性能标准，2）性能参考，3）辅助策略。针对涉及引导虚拟固定装置的场景，比较了辅助策略模块的不同实现方式。使用单用户评估来说明不同实现在确定的辅助级别上的效果，多用户研究则允许对它们进行统计比较。结果表明，自适应辅助策略可能优于恒定辅助策略，切换辅助策略比持续自适应策略具有优势，如果没有单一标准具有突出优先级，则应优先选择多标准性能指标，并且性能参考的选择高度依赖于应用。
