Comparing Machine Learning Approaches for Fall Risk Assessment

Joana Silva, João Madureira, Cláudia Tonelo, Daniela Baltazar, Catarina Silva, Anabela Martins, Carlos Alcobia, Inês Sousa

2017

Abstract

Traditional fall risk assessment tests are based on timing certain physical tasks, such as the timed up and go test, counting the number of repetitions in a certain time-frame, as the 30-second sit-to-stand or observation such as the 4-stage balance test. A systematic comparison of multifactorial assessment tools and their instrumentation for fall risk classification based on machine learning approaches were studied for a population of 296 community-dwelling older persons aged above 50 years old. Using features from inertial sensors and a pressure platform by opposition to using solely the tests scores and personal metrics increased the F-Score of Naïve Bayes classifier from 72.85% to 92.61%. Functional abilities revealed higher association with fall level than personal conditions such as gender, age and health conditions.

Paper Citation

in Harvard Style

Silva J., Madureira J., Tonelo C., Baltazar D., Silva C., Martins A., Alcobia C. and Sousa I. (2017). Comparing Machine Learning Approaches for Fall Risk Assessment. In - BIOSIGNALS, (BIOSTEC 2017) ISBN , pages 0-0. DOI: 10.5220/0006227800001488

in Bibtex Style

@conference{biosignals17,
author={Joana Silva and João Madureira and Cláudia Tonelo and Daniela Baltazar and Catarina Silva and Anabela Martins and Carlos Alcobia and Inês Sousa},
title={Comparing Machine Learning Approaches for Fall Risk Assessment},
booktitle={ - BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006227800001488},
isbn={},
}

in EndNote Style

TY - CONF

JO - - BIOSIGNALS, (BIOSTEC 2017)
TI - Comparing Machine Learning Approaches for Fall Risk Assessment
SN -
AU - Silva J.
AU - Madureira J.
AU - Tonelo C.
AU - Baltazar D.
AU - Silva C.
AU - Martins A.
AU - Alcobia C.
AU - Sousa I.
PY - 2017
SP - 0
EP - 0
DO - 10.5220/0006227800001488