Fall Prediction Model for a Reconfigurable Mobile Support Robot

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Kamienski, Emily

Advisor dc:contributor.advisor

• Asada, Haruhiko Harry

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139039

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139039