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dc.contributor.advisorNeville Hogan.en_US
dc.contributor.authorDa Silva, Davi(Davi Eric)en_US
dc.contributor.otherHarvard--MIT Program in Health Sciences and Technology.en_US
dc.date.accessioned2020-10-18T21:48:33Z
dc.date.available2020-10-18T21:48:33Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/128082
dc.descriptionThesis: S.M., Harvard-MIT Program in Health Sciences and Technology, 2020en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 83-88).en_US
dc.description.abstractHumans are adept at manipulating objects to perform a task, including objects with their own complex dynamics, such as a sloshing cup of coffee or cracking a whip. Humans regularly outperform machines at such physical interaction tasks, in spite of the inferiority of humans' actuation systems. It is therefore of interest to understand what controller or controllers humans use to achieve this proficiency. One major set of hypotheses for human control are derived from optimization theory-that subjects choose trajectories that minimize the time integral of some quantity (such as mean square jerk) across the movement. These hypotheses are appealing for their mathematical simplicity, as they reduce to ordinary differential equations. Another set of hypotheses, however, posits that humans exhibit proficiency in motor tasks by constructing all movements as a superposition of a few smaller "submovements" of a stereotyped shape. Little work has been done investigating what hypothesis best describes human movement in interaction tasks, especially in fast-moving actions where humans' capacity for feedback control is limited. To that end, we used an experimental paradigm where subjects manipulated a virtual cart-pendulum system with the goal of moving it from one position to another while minimizing the residual oscillation of the pendulum at the end of the trial. Experimental velocity trajectories were then decomposed into sums of finite-support lognormal curves to infer what submovements may have been used by the subject to construct the movements. Quantitative information about submovement shape and subjects' movement strategies overall were then extracted from the decompositions. The trajectories themselves were also compared to three models: minimum object crackle (MCO), dynamically constrained minimum jerk of the hand (DCMJH), and input shaping (IS). The former are optimization-based hypotheses, and the latter is more readily compatible with the submovement hypothesisen_US
dc.description.statementofresponsibilityby Davi da Silva.en_US
dc.format.extent88 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectHarvard--MIT Program in Health Sciences and Technology.en_US
dc.titleHuman strategies for manipulation of physical objects with complex dynamicsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.identifier.oclc1199299962en_US
dc.description.collectionS.M. Harvard-MIT Program in Health Sciences and Technologyen_US
dspace.imported2020-10-18T21:48:29Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentHSTen_US


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