Authors:
Mario Sáenz Espinoza
and
Miguel Velhote Correia
Affiliation:
University of Porto and INESC-TEC, Portugal
Keyword(s):
Fall Prediction, Qualitative Assessment, Quantitative Assessment, Wearable Technologies, Screening Tool.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Monitoring and Telemetry
;
Physiological Computing Systems
;
Wearable Sensors and Systems
Abstract:
For the first time in history, the world shows a clear trend towards aging. This poses an intrinsic hazard for
the ever growing population, which becomes more vulnerable to common age-related illnesses and conditions.
One of the most serious risks elders face is falling, as it is responsible for countless admissions to geriatric care
institutions and thousands of deaths each year. In an effort to improve elders’ safety and quality of life many
groups have address the fall prevention issue, coming to several different results as of what variables are the
most important to consider in a fall prediction tool. These variables range from qualitative aspects (history of
falls, dementia, use of medication, etc.) to quantitative ones (total walked distance per day, walking cadence,
center of mass, etc.), but none of them per se seems to deliver a definite and complete answer to the problem
at hand. The paper herein aims to present a new hybrid approach, which combines both the highest co-rel
ated
qualitative and quantitative biovariables in a single tool: the MATHOV + QAVS, which is proposed as a new
fall assessment screening tool and eventually as baseline criteria for a complete elder fall prediction system.
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