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Massachusetts Institute of Technology

Analysis of a shuffling detection device for fall prevention

Abstract

dc:description.abstract

According to the World Health Organization, falls are the second leading cause of accidental or unintentional injury deaths worldwide. In order to address this issue from a fall prevention perspective, I began developing a footwear device to give feedback to the user on their walking. In this iteration, I've identified characteristics to distinguish between shuffling and walking strides for implementation in a threshold-based algorithm and the device created was effective in making this distinction with a 74.65% accuracy and 73.17% sensitivity. The errors identified are those common to threshold-based algorithms and can be corrected in future iterations with a more sensitive threshold and stride detection method. Moving forward, the device can be improved in order to be integrated into a feedback device for the intended user.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Alexander, Alaisha (Alaisha Diahann)
Advisor dc:contributor.advisor
  • Leia Stirling.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/120269
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/120269

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Alexander, Alaisha (Alaisha Diahann). Analysis of a shuffling detection device for fall prevention. Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120269