Data-driven combinatorial optimization for sensor-based assessment of near falls

Falls represent a considerable public health problem, especially in older population. We describe and evaluate data-driven operations research models for detection and situational assessment of falls and near falls with a system of wearable sensors. The models are formulated as instances of the mult...

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Bibliographic Details
Published inAnnals of operations research Vol. 276; no. 1-2; pp. 137 - 153
Main Authors Kammerdiner, Alla R., Guererro, Andre N.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.05.2019
Springer
Springer Nature B.V
Subjects
Accidental falls
Age
Analysis
Assignment problem
Business and Management
Care and treatment
Combinatorial analysis
Combinatorial optimization
Combinatorics
Computational Biomedicine
Economic aspects
Elderly
Experiments
Falls
False alarms
Human subjects
Management science
Operations research
Operations Research/Decision Theory
Optimization
Public health
Ratings & rankings
Sensors
Theory of Computation
Time series
United States
Systems of wearable sensors
The multidimensional assignment problem
Falls and near falls
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Summary:Falls represent a considerable public health problem, especially in older population. We describe and evaluate data-driven operations research models for detection and situational assessment of falls and near falls with a system of wearable sensors. The models are formulated as instances of the multidimensional assignment problem. Our computational studies provide some initial empirical evidence of the potential usefulness of this new application of the multidimensional assignment problem.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-017-2585-1