Fitts’ Law for Human-Human Collaborative Reaching Task
published: May 23, 2017, recorded: April 2017, views: 8
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There is an ongoing trend in robotics for the robots to be placed outside their protective cages to work alongside humans. Furthermore, the robots and humans should work together, achieving better performance and ergonomics. In the field of neuromechanics, several studies  investigated human collaboration while executing a physically interactive task. In a study of Ganesh, G. et al. , multiple pairs of subjects were connected through a pair of haptic robots in an arm reaching task. The subject pairs participating in this study were unaware of the “algorithm” that governed the haptic feedback interface they felt. In other words, the participants were unaware that they were being coupled between each other. This study demonstrated that physical interaction between the partners results in an improved performance of both partners. However, they did not address how the interaction affects partners that are sensomotoricaly aware of each other and physically coupled by a rigid object. In our study we address this question by having subjects aware of their interaction with the other partner. Subject pairs (10 male participants) were coupled with a metal rod which had a cylinder perpendicular to the middle of the rod. The goal of the subject pair was to hit an appearing target on the screen with the cylinder as fast as possible. Subjects were also instructed not to talk to each other in order to focus their attention on visual perception and haptic feedback. Analysis of the data acquired by our experiment shows that the reaching motion of the subject pairs can be sufficiently described by a wellknown speed-accuracy trade off model, i.e. Fitts’ law  which was primarily used for the single hand reaching tasks. Specifically, the time needed to hit a target for a human pair follows the same principles as found in a single-person reaching tasks. For our future work, we aim to exploit our experimental findings to improve the control algorithms for robots in a human-robot cooperative setups. This will increase efficiency in a human-robot cooperative scenario and potentially enable the human partner to improve his/hers task performance skills.
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