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On the Fear of Falling Detection by Moving Horizon Estimation
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-6605-1204
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-0079-9049
2020 (English)In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 16512-16517Conference paper, Published paper (Refereed)
Abstract [en]

Fear of falling (FoF) is a major health problem, especially in elders, which can lead to falls, injury, loss of independence, and premature needs of nursing and assistance. However, most of the studies have focused on the psychological aspect of the FoF and there is a significant lack of technological assistance and methodology to detect and eliminate the effects of this fear on maintaining balance. In this article, we propose a novel method to detect the FoF as a quantitative signal. In our proposed novel approach, fear is considered as an internal disturbance inside a Central Nervous System (CNS) that can affect the generated output torque to each joint of the psychical body. By assuming the human body in a quiet stance, as an inverted pendulum model, this disturbance signal is estimated by Moving Horizon Estimation (MHE). For this purpose, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails, as well as the psychological FoF falls efficacy test, were collected and utilized for the estimation and validation of the results. The experimental results show that the subjects with FoF present a higher variation in the estimated signal. This method can sufficiently detect the FoF by the posturographic and motion data, which can be utilized on the future assistive devices for prevention and treatment of the FoF and falls.

Place, publisher, year, edition, pages
Elsevier, 2020. p. 16512-16517
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 53 (2)
Keywords [en]
Fear Estimation, Biomedical System, Quantification of physiological parameters for diagnosis, treatment assessment, Balance, Estimation
National Category
Signal Processing
Research subject
Automatic Control; Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-79267DOI: 10.1016/j.ifacol.2020.12.759ISI: 000652593600523Scopus ID: 2-s2.0-85119621853OAI: oai:DiVA.org:ltu-79267DiVA, id: diva2:1437007
Conference
21st IFAC World Congress, Berling, Germany, July 11-17, 2020
Funder
Swedish Research Council, K2015-99X-22756-01-4Available from: 2020-06-08 Created: 2020-06-08 Last updated: 2025-10-22Bibliographically approved

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Jafari, HedyehMansouri, Sina SharifNikolakopoulos, GeorgeGustafsson, Thomas

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