Enhancing User Performance by Adaptively Changing Haptic Feedback Cues in a Fitts's Law Task

Abstract

Enhancing human user performance in some complex task is an important research question in many domains from skilled manufacturing to rehabilitation and surgical training. Many examples in the literature explore the effects of both haptic assistance or guidance to complete a task, as well as haptic hindrance to temporarily increase task difficulty for the ultimate goal of faster learning. Studies also suggest adaptively changing guidance based on expertise may be most effective. However, to our knowledge, there has not yet been a conclusive study evaluating these enhancement modes in a systematic experiment. In this article, we evaluate learning outcomes for 24 human subjects in a randomized control trial performing a Fitt's law reaching task under various haptic feedback conditions including: no haptics, assistive haptics, resistive haptics, and adaptively changing haptics tied to current performance measures. Subjects each performed 400 trials total and this paper reports results for 40 pre-test and 40 post-test trials. While most conditions did show improvements in performance, we found statistically significant results indicating that our adaptive haptic feedback condition leads to faster and more effective learning as evidenced by metrics of movement time, overshoot, performance index, and speed when compared to the other groups.