Negotiated control between the manual and visual systems for visually guided hand reaching movements.

K Han Kim,R Brent Gillespie,Bernard J Martin

doi:10.1186/1743-0003-11-102

Abstract

BackgroundControl of reaching movements for manual work, vehicle operation, or interactions with manual interfaces requires concurrent gaze control for visual guidance of the hand. We hypothesize that reaching movements are based on negotiated strategies to resolve possible conflicting demands placed on body segments shared by the visual (gaze) and manual (hand) control systems. Further, we hypothesize that a multiplicity of possible spatial configurations (redundancy) in a movement system enables a resolution of conflicting demands that does not require sacrificing the goals of the two systems.MethodsThe simultaneous control of manual reach and gaze during seated reaching movements was simulated by solving an inverse kinematics model wherein joint trajectories were estimated from a set of recorded hand and head movements. A secondary objective function, termed negotiation function, was introduced to describe a means for the manual reach and gaze directing systems to balance independent goals against (possibly competing) demands for shared resources, namely the torso movement. For both systems, the trade-off may be resolved without sacrificing goal achievement by taking advantage of redundant degrees of freedom. Estimated joint trajectories were then compared to joint movement recordings from ten participants. Joint angles were predicted with and without the negotiation function in place, and model accuracy was determined using the root-mean-square errors (RMSEs) and differences between estimated and recorded joint angles.ResultsThe prediction accuracy was generally improved when negotiation was included: the negotiated control reduced RMSE by 16% and 30% on average when compared to the systems with only manual or visual control, respectively. Furthermore, the RMSE in the negotiated control system tended to improve with torso movement amplitude.ConclusionsThe proposed model describes how multiple systems cooperate to perform goal-directed human movements when those movements draw upon shared resources. Allocation of shared resources can be undertaken by a negotiation process that is aware of redundancies and the existence of multiple solutions within the individual systems.

Highlights

Control of reaching movements for manual work, vehicle operation, or interactions with manual interfaces requires concurrent gaze control for visual guidance of the hand
Visual information provides feedback for accurate guidance of movements [1,2] and plays an important role in the generation of movement plans as well as the calibration of movement systems with respect to the environment [3,4,5]. These perspectives suggest that reaching movements involve the coordination of two effector systems: the hand being guided to the target and the gaze being directed at the target
Seven revolute joints corresponding to the hand, forearm, upper arm and torso comprise the manual system variables, which can be represented by an angle vector θm = [θm1, θm2, θm3, θm4, θm5, θm6, θm7]T

Summary

Introduction

Control of reaching movements for manual work, vehicle operation, or interactions with manual interfaces requires concurrent gaze control for visual guidance of the hand. Visual information provides feedback for accurate guidance of movements [1,2] and plays an important role in the generation of movement plans as well as the calibration of movement systems with respect to the environment [3,4,5]. These perspectives suggest that reaching movements involve the coordination of two effector systems: the hand being guided to the target and the gaze being directed at the target. The CNS must solve complex problems from multiple layers of reference frame conversions [6], spatial transformation [7] and sensory-motor integration through feedback and feed-forward control [8]

Methods

Discussion

Conclusion