Fall Prediction Model for a Reconfigurable Mobile Support Robot
Name
kamienski-emilyk-smme-meche-2021-thesis.pdf
Description
Thesis PDF
Size
5.45 MB
Format
Adobe PDF
Checksum (MD5)
f2a0b010fed139c80ffc7c38b039469b
Author(s)
Kamienski, Emily
Advisor(s)
Asada, Haruhiko Harry
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
