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Authors: Joana Silva 1 ; João Madureira 1 ; Cláudia Tonelo 2 ; Daniela Baltazar 3 ; Catarina Silva 3 ; Anabela Martins 3 ; Carlos Alcobia 2 and Inês Sousa 1

Affiliations: 1 Fraunhofer Portugal AICOS, Portugal ; 2 Sensing Future Technologies, Portugal ; 3 ESTeSC Coimbra Health School, Portugal

ISBN: 978-989-758-212-7

Keyword(s): Fall Risk Assessment, Inertial Sensors, Pressure Platform, Timed-up and Go Test, Sit-to-Stand, 4-Stage Test, Machine Learning, Classification, Regression.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

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.

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Paper citation in several formats:
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 Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 223-230. DOI: 10.5220/0006227802230230

@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={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={223-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006227802230230},
isbn={978-989-758-212-7},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Comparing Machine Learning Approaches for Fall Risk Assessment
SN - 978-989-758-212-7
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 - 223
EP - 230
DO - 10.5220/0006227802230230

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