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Predicting balance impairments in older adults: a wavelet-based center of pressure classification approach
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: 0000-0002-0079-9049
Luleå University of Technology, Department of Health, Learning and Technology, Health, Medicine and Rehabilitation.ORCID iD: 0000-0002-9813-2719
Luleå University of Technology, Department of Health, Learning and Technology, Health, Medicine and Rehabilitation.ORCID iD: 0000-0003-3901-0364
2023 (English)In: Biomedical engineering online, E-ISSN 1475-925X, Vol. 22, article id 83Article in journal (Refereed) Published
Abstract [en]

Background: Aging is associated with a decline in postural control and an increased risk of falls. The Center of Pressure (CoP) trajectory analysis is a commonly used method to assess balance. In this study, we proposed a new method to identify balance impairments in older adults by analyzing their CoP trajectory frequency components, sensory inputs, reaction time, motor functions, and Fall-related Concerns (FrC).

Methods: The study includes 45 older adults aged 75.2(±4.5)75.2(±4.5) years who were assessed for sensory and motor functions. FrC and postural control in a quiet stance with open and closed eyes on stable and unstable surfaces. A Discrete Wavelet Transform (DWT) was used to detect features in frequency scales, followed by the K-means algorithm to detect different clusters. The multinomial logistic model was used to identify and predict the association of each group with the sensorimotor tests and FrC.

Results: The study results showed that by DWT, three distinct groups of subjects could be revealed. Group 2 exhibited the broadest use of frequency scales, less decline in sensorimotor functions, and lowest FrC. The study also found that a decline in sensorimotor functions and fall-related concern may cause individuals to rely on either very low-frequency scales (group 1) or higher-frequency scales (group 3) and that those who use lower-frequency scales (group 1) can manage their balance more successfully than group 3.

Conclusions: Our study provides a new, cost-effective method for detecting balance impairments in older adults. This method can be used to identify people at risk and develop interventions and rehabilitation strategies to prevent falls in this population.

Place, publisher, year, edition, pages
Springer Nature, 2023. Vol. 22, article id 83
Keywords [en]
Balance, Wavelet analysis, Clustering, Classification, Sensorimotor, Ageing
National Category
Physiotherapy
Research subject
Automatic Control; Physiotherapy
Identifiers
URN: urn:nbn:se:ltu:diva-97243DOI: 10.1186/s12938-023-01146-3ISI: 001052826900001PubMedID: 37608334Scopus ID: 2-s2.0-85168702366OAI: oai:DiVA.org:ltu-97243DiVA, id: diva2:1757608
Funder
Swedish Research Council, K2015-99X-22756-01-4
Note

Validerad;2023;Nivå 2;2023-09-04 (hanlid)

Available from: 2023-05-17 Created: 2023-05-17 Last updated: 2025-10-21Bibliographically approved

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Jafari, HedyehGustafsson, ThomasNyberg, LarsRöijezon, Ulrik

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