The Influence of Anthropometric Variables and Filtering Frequency on Center of Pressure Data

Koltermann, Jan Jens; Floessel, Philipp; Hammerschmidt, Franziska; Disch, Alexander C.

doi:10.3390/s23115105

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…It is worth noting that 99% of the frequency spectrum of the CoP signal ranges from 0–6.5 Hz [ 39 ]. During the first experimental trials, we detected that the OREKA platform’s own kinematic movement introduced some perturbances at higher frequencies.…”

Section: Methodsmentioning

confidence: 99%

On-Screen Visual Feedback Effect on Static Balance Assessment with Perturbations

Valenzuela,

Corral,

Diez

et al. 2024

Sensors

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In this study, the novel mobile dynamometric platform, OREKA, was utilized to perform an extensive analysis of the centre of pressure behaviour during different tilt motion exercises. This platform is based on a parallel manipulator mechanism and can perform rotations around both horizontal axes and a vertical translation. A group of participants took part in an experimental campaign involving the completion of a set of exercises. The aim was to evaluate the platform’s potential practical application and investigate the impact of visual on-screen feedback on centre of pressure motion through multiple balance indicators. The use of the OREKA platform enables the study of the impact on a user’s balance control behaviour under different rotational perturbations, depending on the availability of real-time visual feedback on a screen. Furthermore, it presented data identifying postural control variations among clinically healthy individuals. These findings are fundamental to comprehending the dynamics of body balance. Further investigation is needed to explore these initial findings and fully unlock the potential of the OREKA platform for balance assessment methodologies.

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Section: Methodsmentioning

confidence: 99%

On-Screen Visual Feedback Effect on Static Balance Assessment with Perturbations

Valenzuela,

Corral,

Diez

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The measurement setup and data processing are shown schematically in Figure 1. and the measurement variable is not changed [36]. After this procedure, the CoP curve was determined.…”

Section: Methodsmentioning

confidence: 99%

“…The cut-off frequency was determined for the entire cohort according to the method of Koltermann et al, 2019 [35]. This ensures that the relationship between the anthropometry and the measurement variable is not changed [36]. After this procedure, the CoP curve was determined.…”

Section: Methodsmentioning

confidence: 99%

A Statistical and AI Analysis of the Frequency Spectrum in the Measurement of the Center of Pressure Track in the Seated Position in Healthy Subjects and Subjects with Low Back Pain

Koltermann,

Floessel,

Hammerschmidt

et al. 2024

Sensors

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Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and consideration of the results in the spectrum in conjunction with an AI-supported evaluation could represent an alternative diagnostic method quantifying neuromuscular control. In a prospective cross-sectional study, 188 subjects aged between 18 and 60 years were recruited and divided into two groups: “LowBackPain” vs. “Healthy”. Subsequently, measurements of postural control in a seated position were carried out for 60 s using a modified balance board. A spectrum per trail was calculated using the measured CoP tracks in the range from 0.01 to 10 Hz. Various algorithms for data classification and prediction of these classes were tested for the parameter combination with the highest proven static influence on the parameter pain. The best results were found in a frequency spectrum of 0.001 Hz and greater than 1 Hz. After transforming the track from the time domain to the image domain for representation as power density, the influence of pain was highly significant (effect size 0.9). The link between pain and gender (p = 0.015) and pain and height (p = 0.012) also demonstrated significant results. The assessment of postural control in a seated position allows differentiation between “LowBackPain” and “Healthy” subjects. Using the AI algorithm of neural networks, the data set can be correctly differentiated into “LowBackPain” and “Healthy” with a probability of 81%.

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The Influence of Anthropometric Variables and Filtering Frequency on Center of Pressure Data

Cited by 2 publications

References 30 publications

On-Screen Visual Feedback Effect on Static Balance Assessment with Perturbations

On-Screen Visual Feedback Effect on Static Balance Assessment with Perturbations

A Statistical and AI Analysis of the Frequency Spectrum in the Measurement of the Center of Pressure Track in the Seated Position in Healthy Subjects and Subjects with Low Back Pain

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