A detection system to monitor and manage falls among wheelchair users: Phase 1

Principal Investigator(s):  View help for Principal Investigator(s) Laura Rice, University of Illinois at Urbana-Champaign; Libak Abou, University of Illinois at Urbana-Champaign; Jacob Sosnoff, Kansas University Medical Center; Peter Presti, Georgia Institute of Technology; Alexander Fliflet, University of Illinois at Urbana-Champaign

Version:  View help for Version V1

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Project Description

Summary:  View help for Summary Falls events are common among wheelchair users, and associated with injury, declines in performance of activities of daily living, hospital admissions and admission to long term care facilities. To facilitate successful aging-in-place among individuals in the target population, this project will develop a smart, multi-component system that will accurately detect falls from a wheelchair and notify caregivers to enable a quick response to reduce the amount of time an individual spends lying (“lie time”) on the ground. The first phase of this study (Phase #1) will examine fall acceleration patterns from a wheelchair. This data will be used to develop a prototype customizable fall detection system that is able to accurately detect a fall from a wheelchair. The development and testing of the fall detection prototype will be completed at a later date and is not described in this IRB application.

Scope of Project

Subject Terms:  View help for Subject Terms Wheelchair; fall; detection; Sensor; accelerometer
Geographic Coverage:  View help for Geographic Coverage United States
Universe:  View help for Universe Adults able-bodied between 18-30 years old.
Data Type(s):  View help for Data Type(s) experimental data

Methodology

Sampling:  View help for Sampling 30 individuals were recruited
Collection Mode(s):  View help for Collection Mode(s) other
Scales:  View help for Scales Data from accelerometer placed at the chest, wrist, and back head.
Unit(s) of Observation:  View help for Unit(s) of Observation m/s2

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Published Versions

V1 [2022-01-07]

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