{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147474"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147474","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Human Interaction with Various Elliptical Constraints","abstract":"Despite worse actuators and long feedback delays, humans outperform robots in a number of tasks, including tool use and physical interaction. In order to study the human controller, this work explores interaction with three different elliptical kinematic constraints. The shapes of these constraints were based on prior work: the circle was the same size as a previous crank-turning study; the aligned ellipse was based on estimates of the zero-force trajectory during circular crank-turning; the anti-aligned ellipse was a rotated version of the aligned ellipse. Subjects were given visual feedback on their velocity to decrease variability across trials, constraint shapes, and subjects. The velocity they were asked to track had a speed-curvature profile that was consistent with the two-thirds power law, which is widely reported in human motion. Hypotheses about the experiment were made by modeling human physical interaction with a Norton equivalent network. The anti-aligned ellipse was hypothesized to evoke normal force and velocity error values higher than the circle, and the circle was hypothesized to evoke higher normal force and velocity error values than the aligned ellipse. Statistical analysis revealed that the anti-aligned ellipse did evoke higher normal force and velocity errors than both the circle and aligned ellipse. There was no significant difference between the circle and aligned ellipse for either normal force or velocity error. The experiment was a success, however, in proving that constraint shape does affect the force that subjects exert on the constraint. This result suggests several further studies on human interaction with various kinematic constraints.","abstract_html":"Despite worse actuators and long feedback delays, humans outperform robots in a number of tasks, including tool use and physical interaction. In order to study the human controller, this work explores interaction with three different elliptical kinematic constraints. The shapes of these constraints were based on prior work: the circle was the same size as a previous crank-turning study; the aligned ellipse was based on estimates of the zero-force trajectory during circular crank-turning; the anti-aligned ellipse was a rotated version of the aligned ellipse. Subjects were given visual feedback on their velocity to decrease variability across trials, constraint shapes, and subjects. The velocity they were asked to track had a speed-curvature profile that was consistent with the two-thirds power law, which is widely reported in human motion. Hypotheses about the experiment were made by modeling human physical interaction with a Norton equivalent network. The anti-aligned ellipse was hypothesized to evoke normal force and velocity error values higher than the circle, and the circle was hypothesized to evoke higher normal force and velocity error values than the aligned ellipse. Statistical analysis revealed that the anti-aligned ellipse did evoke higher normal force and velocity errors than both the circle and aligned ellipse. There was no significant difference between the circle and aligned ellipse for either normal force or velocity error. The experiment was a success, however, in proving that constraint shape does affect the force that subjects exert on the constraint. This result suggests several further studies on human interaction with various kinematic constraints.","abstract_has_math":false,"creators":["Arons, Nicolas"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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In order to study the human controller, this work explores interaction with three different elliptical kinematic constraints. The shapes of these constraints were based on prior work: the circle was the same size as a previous crank-turning study; the aligned ellipse was based on estimates of the zero-force trajectory during circular crank-turning; the anti-aligned ellipse was a rotated version of the aligned ellipse. Subjects were given visual feedback on their velocity to decrease variability across trials, constraint shapes, and subjects. The velocity they were asked to track had a speed-curvature profile that was consistent with the two-thirds power law, which is widely reported in human motion. Hypotheses about the experiment were made by modeling human physical interaction with a Norton equivalent network. The anti-aligned ellipse was hypothesized to evoke normal force and velocity error values higher than the circle, and the circle was hypothesized to evoke higher normal force and velocity error values than the aligned ellipse. Statistical analysis revealed that the anti-aligned ellipse did evoke higher normal force and velocity errors than both the circle and aligned ellipse. There was no significant difference between the circle and aligned ellipse for either normal force or velocity error. The experiment was a success, however, in proving that constraint shape does affect the force that subjects exert on the constraint. This result suggests several further studies on human interaction with various kinematic constraints."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Human Interaction with Various Elliptical Constraints"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hogan, Neville"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Arons, Nicolas"],"dc:date.accessioned":["2023-01-19T19:52:51Z"],"dc:date.available":["2023-01-19T19:52:51Z"],"dc:date.issued":["2022-09"],"dc:description.abstract":["Despite worse actuators and long feedback delays, humans outperform robots in a number of tasks, including tool use and physical interaction. In order to study the human controller, this work explores interaction with three different elliptical kinematic constraints. The shapes of these constraints were based on prior work: the circle was the same size as a previous crank-turning study; the aligned ellipse was based on estimates of the zero-force trajectory during circular crank-turning; the anti-aligned ellipse was a rotated version of the aligned ellipse. Subjects were given visual feedback on their velocity to decrease variability across trials, constraint shapes, and subjects. The velocity they were asked to track had a speed-curvature profile that was consistent with the two-thirds power law, which is widely reported in human motion. Hypotheses about the experiment were made by modeling human physical interaction with a Norton equivalent network. The anti-aligned ellipse was hypothesized to evoke normal force and velocity error values higher than the circle, and the circle was hypothesized to evoke higher normal force and velocity error values than the aligned ellipse. Statistical analysis revealed that the anti-aligned ellipse did evoke higher normal force and velocity errors than both the circle and aligned ellipse. There was no significant difference between the circle and aligned ellipse for either normal force or velocity error. The experiment was a success, however, in proving that constraint shape does affect the force that subjects exert on the constraint. This result suggests several further studies on human interaction with various kinematic constraints."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/147474"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Human Interaction with Various Elliptical Constraints"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-22T22:20:51Z"}