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dc.contributor.authorKoeppen, Ryan P.
dc.contributor.authorHuber, Meghan E
dc.contributor.authorSternad, Dagmar
dc.contributor.authorHogan, Neville
dc.date.accessioned2018-12-03T17:12:23Z
dc.date.available2018-12-03T17:12:23Z
dc.date.issued2017-10
dc.identifier.isbn978-0-7918-5827-1
dc.identifier.urihttp://hdl.handle.net/1721.1/119392
dc.description.abstractPhysical interaction with tools is ubiquitous in functional activities of daily living. While tool use is considered a hallmark of human behavior, how humans control such physical interactions is still poorly understood. When humans perform a motor task, it is commonly suggested that the central nervous system coordinates the musculo-skeletal system to minimize muscle effort. In this paper, we tested if this notion holds true for motor tasks that involve physical interaction. Specifically, we investigated whether humans minimize muscle forces to control physical interaction with a circular kinematic constraint. Using a simplified arm model, we derived three predictions for how humans should behave if they were minimizing muscular effort to perform the task. First, we predicted that subjects would exert workless, radial forces on the constraint. Second, we predicted that the muscles would be deactivated when they could not contribute to work. Third, we predicted that when moving very slowly along the constraint, the pattern of muscle activity would not differ between clockwise (CW) and counterclockwise (CCW) motions. To test these predictions, we instructed human subjects to move a robot handle around a virtual, circular constraint at a constant tangential velocity. To reduce the effect of forces that might arise from incomplete compensation of neuro-musculoskeletal dynamics, the target tangential speed was set to an extremely slow pace (~1 revolution every 13.3 seconds). Ultimately, the results of human experiment did not support the predictions derived from our model of minimizing muscular effort. While subjects did exert workless forces, they did not deactivate muscles as predicted. Furthermore, muscle activation patterns differed between CW and CCW motions about the constraint. These findings demonstrate that minimizing muscle effort is not a significant factor in human performance of this constrained-motion task. Instead, the central nervous system likely prioritizes reducing other costs, such as computational effort, over muscle effort to control physical interactions.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (R01-HD087089)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). National Robotics Initiative (NSF-NRI 1637824)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). EArly-concept Grants for Exploratory Research (NSF-EAGER-1548501)en_US
dc.description.sponsorshipEric P. and Evelyn E. Newman Funden_US
dc.description.sponsorshipGloria Blake Endowment Funden_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (R01-HD081346)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). National Robotics Initiative (NSF-NRI 1637854)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). EArly-concept Grants for Exploratory Research (NSF-EAGER-1548514)en_US
dc.publisherASME Internationalen_US
dc.relation.isversionofhttp://dx.doi.org/10.1115/DSCC2017-5202en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceASMEen_US
dc.titleControlling Physical Interactions: Humans Do Not Minimize Muscle Efforten_US
dc.typeArticleen_US
dc.identifier.citationKoeppen, Ryan, Meghan E. Huber, Dagmar Sternad, and Neville Hogan. “Controlling Physical Interactions: Humans Do Not Minimize Muscle Effort.” Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems (October 11, 2017).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorKoeppen, Ryan P.
dc.contributor.mitauthorHuber, Meghan E
dc.contributor.mitauthorSternad, Dagmar
dc.contributor.mitauthorHogan, Neville
dc.relation.journalVolume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systemsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2018-11-30T19:02:24Z
dspace.orderedauthorsKoeppen, Ryan; Huber, Meghan E.; Sternad, Dagmar; Hogan, Nevilleen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5366-2145
mit.licensePUBLISHER_POLICYen_US


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