Effect of interpolation on parameters extracted from seating interface pressure arrays.

INTRODUCTION The force-sensing array has a long-recognized utility for not only scientific inquiry [1], but also clinical care. Pressure mapping has come to be regarded as a "best practice" in prevention of pressure ulcers in the clinical care setting [2]. While the pressure map is a useful tool for visualization and qualitative assessment of the pressure distribution across the seating surface, extraction of quantitative data from pressure arrays is becoming increasingly common. While there remain many significant reservations in the scientific community about the use of pressure mapping as a quantitative modality, it is becoming increasingly popular in the study of the seating interface. For example, expression of the interface pressure via the dispersion index allows for objective measure of the evenness of load distribution and has been used to inform clinical recommendations [3]. Features extracted from pressure maps have been useful in a wide variety of clinical applications relevant to wheelchair use, particularly in pressure ulcer prevention and management [4-6]. Indeed, objective measures derived from pressure array data are lately considered sufficiently robust as to be utilized as primary outcome measures in randomized clinical trials [7-8]. A great need exists to continue to develop reliable and sensitive parameters and to refine methods for processing and analyzing these data. Whereas metrics extracted from pressure array data are sensitive to the methods by which the arrays are processed, special care must be taken to understand the outcome of each processing step and how it might alter the measurement. Furthermore, while the approaches for processing pressure array data vary widely, it is possible that the amount of information that can be integrated across studies would be greatly increased by a robust understanding of the effect of each link in the signal processing chain [9]. Here, focused study is applied to one particular processing step that is endemic to the analysis of pressure array data, i.e., interpolation, in the interest of better understanding the effect of the degree and ordering of an interpolation transformation on conventional features extracted from pressure maps. Interpolation is a technique used to estimate data points where no data were collected [10]. In particular application to pressure array data used for assessment of the interface pressure distribution while seated, interpolation has the effect of increasing sampling resolution of a low spatial resolution measurement modality. A great many of the commercially available pressure mats contain only 256 sensors in a 16 x 16 grid, and interpolation provides a smoother, more interpretable high-resolution image of the buttock anatomy. Interpolation is a method de rigueur in the study of seat pressure and has been widely used for the purposes of both visualization and prediction for more than 25 yr [11]. There are many different interpolation techniques, and a comprehensive review is beyond the scope of this work. However, two approaches have emerged as most commonly used in the analysis of seating data: linear and cubic spline. In short summary, the linear (more formally, bilinear interpolation in the context of two-dimensional array data) involves a simple point-slope imputation of data at points intermediate to true observation; the cubic spline (more formally, bicubic spline) involves a summed, iterative approximation through fitting a third-order polynomial [12]. Naturally, the method by which a data set is interpolated will affect the values of the interpolants. Different methods of interpolation may also alter the values of the observed data (acting, in a way, as a separate filter). For example, in a single-dimensional data set extracted from a row of sensors in a 16 x 16 pressure mat, 16 sensors are interpolated out to 31 sensors via linear (Figure (a)) and cubic (Figure (b)) interpolation. …