Human Behavioral Metrics of a Predictive Model Emerging During Robot Assisted Following Without Visual Feedback

Anuradha, Ranasinghe and Prokar, Dasgupta and Atulya, Nagar and Thrishantha, Nanayakkara (2018) Human Behavioral Metrics of a Predictive Model Emerging During Robot Assisted Following Without Visual Feedback. IEEE Robotics and Automation Letters. ISSN 2377-3766 (Accepted for Publication)

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Official URL: https://ieeexplore.ieee.org/document/8328894/

Abstract

Robot assisted guiding is gaining increased interest due to many applications involving moving in noisy and low visibility environments. In such cases, haptic feedback is the most effective medium to communicate. In this paper, we focus on perturbation based haptic feedback due to applications like guide dogs for visually impaired people and potential robotic counterparts providing haptic feedback via reins to assist indoor firefighting in thick smoke. Since proprioceptive sensors like spindles and tendons are part of the muscles involved in the perturbation, haptic perception becomes a coupled phenomenon with spontaneous reflex muscle activity. The nature of this interplay and how the model based sensory-motor integration evolves during haptic based guiding is not well understood yet. In this study, we asked human followers to hold the handle of a hard rein attached to a 1-DoF robotic arm that gave perturbations to the hand to correct an angle error of the follower. We found that human followers start with a 2nd order reactive autoregressive following model and changes it to a predictive model with training. The post-perturbation Electromyography (EMG) activity exhibited a reduction in co-contraction of muscles with training. This was accompanied by a reduction in the leftward/rightward asymmetry of a set of followers behavioural metrics. These results show that the model based prediction accounts for the internal coupling between proprioception and muscle activity during perturbation responses. Furthermore, the results provide a firm foundation and measurement metrics to design and evaluate robot assisted haptic guiding of humans in low visibility environments.

Item Type: Article
Additional Information and Comments: Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Anuradha Dissanayake Mudiyanselage
Date Deposited: 11 Apr 2018 10:25
Last Modified: 11 Apr 2018 10:25
URI: http://hira.hope.ac.uk/id/eprint/2430

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