{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139039"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139039","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Fall Prediction Model for a Reconfigurable Mobile Support Robot","abstract":"This work presents a fall prediction model to be used in conjunction with a reconfigurable robot for elderly mobility support. The fall prediction model is based on a Long Short Term Memory network. A predicted fall will inform a reconfigurable robot to expand its base of support to avoid possible tipping induced by the fall. A wearable support interface consisting of an instrumented harness and auto retracting cable system is developed for supporting the body and preventing a fall from occurring. The prediction model was developed using data taken of simulated falls and activities of daily living while using a test platform with the wearable support interface solution. The reconfigurable robot concept explored resembles a walker and provides mobility assistance during normal use, but it can also expand its base of support during a falling emergency. The model results show that a fall can be predicted 530 ms from the initial observance of instability in the user, which allows sufficient time to reconfigure a robot into a more stable configuration.","abstract_html":"This work presents a fall prediction model to be used in conjunction with a reconfigurable robot for elderly mobility support. The fall prediction model is based on a Long Short Term Memory network. A predicted fall will inform a reconfigurable robot to expand its base of support to avoid possible tipping induced by the fall. A wearable support interface consisting of an instrumented harness and auto retracting cable system is developed for supporting the body and preventing a fall from occurring. The prediction model was developed using data taken of simulated falls and activities of daily living while using a test platform with the wearable support interface solution. The reconfigurable robot concept explored resembles a walker and provides mobility assistance during normal use, but it can also expand its base of support during a falling emergency. The model results show that a fall can be predicted 530 ms from the initial observance of instability in the user, which allows sufficient time to reconfigure a robot into a more stable configuration.","abstract_has_math":false,"creators":["Kamienski, Emily"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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The fall prediction model is based on a Long Short Term Memory network. A predicted fall will inform a reconfigurable robot to expand its base of support to avoid possible tipping induced by the fall. A wearable support interface consisting of an instrumented harness and auto retracting cable system is developed for supporting the body and preventing a fall from occurring. The prediction model was developed using data taken of simulated falls and activities of daily living while using a test platform with the wearable support interface solution. The reconfigurable robot concept explored resembles a walker and provides mobility assistance during normal use, but it can also expand its base of support during a falling emergency. The model results show that a fall can be predicted 530 ms from the initial observance of instability in the user, which allows sufficient time to reconfigure a robot into a more stable configuration."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Fall Prediction Model for a Reconfigurable Mobile Support Robot"]}]}],"canonical_facts":{"dc:contributor.advisor":["Asada, Haruhiko Harry"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Kamienski, Emily"],"dc:date.accessioned":["2022-01-14T14:46:13Z"],"dc:date.available":["2022-01-14T14:46:13Z"],"dc:date.issued":["2021-06"],"dc:description.abstract":["This work presents a fall prediction model to be used in conjunction with a reconfigurable robot for elderly mobility support. The fall prediction model is based on a Long Short Term Memory network. A predicted fall will inform a reconfigurable robot to expand its base of support to avoid possible tipping induced by the fall. A wearable support interface consisting of an instrumented harness and auto retracting cable system is developed for supporting the body and preventing a fall from occurring. The prediction model was developed using data taken of simulated falls and activities of daily living while using a test platform with the wearable support interface solution. The reconfigurable robot concept explored resembles a walker and provides mobility assistance during normal use, but it can also expand its base of support during a falling emergency. The model results show that a fall can be predicted 530 ms from the initial observance of instability in the user, which allows sufficient time to reconfigure a robot into a more stable configuration."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/139039"],"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":["Fall Prediction Model for a Reconfigurable Mobile Support Robot"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-22T22:22:02Z"}