NDLI: Distributions in the error space: Goal-directed movements described in time and state-space representations

Content Provider	IEEE Xplore Digital Library
Author	Fisher, M.E. Huang, F.C. Wright, Z.A. Patton, J.L.
Copyright Year	2014
Description	Author affiliation: Rehabilitation Inst. of Chicago, Chicago, IL, USA (Huang, F.C.) \|\| Univ. of Illinois at Chicago, Chicago, IL, USA (Fisher, M.E.; Wright, Z.A.; Patton, J.L.)
Abstract	Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6953
Ending Page	6956
File Size	2428527
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945227
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Predictive models Standards Data models Visualization Motor drives Measurement
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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