ArticleBiomedical engineering online2023
Predicting balance impairments in older adults: a wavelet-based center of pressure classification approach.
Article in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Wavelet analysis of postural stability reveals age-related differences in spectral response patterns during adaptation to immersive virtual reality.Scientific reports · 2026Article
- Influence of cognitive function on postural control in physically independent older women: a time and time-frequency domain analysis.BMC geriatrics · 2026Article
- Dual-Stream deep learning for multimodal feature fusion and classification of balance control in elite freestyle aerial skiers.PloS one · 2026Article
- Identifying Frailty Risk in Older Adults: The Predictive Value of Functional Tests and Center-of-Pressure-Based Postural Metrics.Journal of clinical medicine · 2025Article
- Quantification of changes in balance control with tasks and injury using detrending methods for time series analysis.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAging 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).
methodsThe study includes 45 older adults aged [Formula: see text] 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.
resultsThe 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.
conclusionsOur 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.
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