Reaching is Better When You Get What You Want: Realtime Feedback of Intended Reaching Trajectory Despite an Unstable Environment.

Justin Horowitz,James Patton,Christine Massie,Tejas Madhavan

doi:10.3389/fnbeh.2015.00365

Justin Horowitz, James Patton + Show 2 more

Open Access

https://doi.org/10.3389/fnbeh.2015.00365

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Abstract

Improvements in human-machine interaction may help overcome the unstable and uncertain environments that cause problems in everyday living. Here we experimentally evaluated intent feedback (IF), which estimates and displays the human operator's underlying intended trajectory in real-time. IF is a filter that combines a model of the arm with position and force data to determine the intended position. Subjects performed targeted reaching motions while seeing either their actual hand position or their estimated intent as a cursor while they experienced white noise forces rendered by a robotic handle. We found significantly better reaching performance during force exposure using the estimated intent. Additionally, in a second set of subjects with a reduced modeled stiffness, IF reduced estimated arm stiffness to about half that without IF, indicating a more relaxed state of operation. While visual distortions typically degrade performance and require an adaptation period to overcome, this particular distortion immediately enhanced performance. In the future, this method could provide novel insights into the nature of control. IF might also be applied in driving and piloting applications to best follow a person's desire in unpredictable or turbulent conditions.

Highlights

Humans often interact with machines in uncertain and complicated environments, such as crowds and traffic, where they must contend with turbulence, moving obstacles, distractions, and disturbances
The work presented here highlights the use of a novel visual distortion of the cursor that leads to superior performance in a hand-eye coordination task in the presence of random disturbances
While other visual distortions typically degrade performance and require an adaptation period to overcome, intent feedback (IF) immediately enhanced performance. This type of feedback may be a new method for enhancing performance in human-machine interactions, and sheds light on how the nervous system uses visual feedback

Summary

Introduction

Humans often interact with machines in uncertain and complicated environments, such as crowds and traffic, where they must contend with turbulence, moving obstacles, distractions, and disturbances. There is the possibility, to exploit additional information from instruments— fast and accurate force sensors—that can measure human machine interactions. While other components of a movement, such as its goal, are intended (Mirabella, 2014), our work here addresses only the intended trajectory. This intent provides new ways to understand the nature of control and provide novel feedback

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