Product limit estimation for capturing of pressure distribution dynamics

Abstract

Measurement of contact pressures at the wheelchair-seating interface is a critically important approach for laboratory research and clinical application in monitoring risk for pressure ulceration. As yet, measures obtained from pressure mapping are static in nature: there is no accounting for changes in pressure distribution over time, despite the well-known interaction between time and pressure in risk estimation. Here, we introduce the first dynamic analysis for distribution of pressure data, based on the Kaplan–Meier (KM) Product Limit Estimator (PLE) a ubiquitous tool encountered in clinical trials and survival analysis. In this approach, the pressure array-over-time data set is sub-sampled two frames at a time (random pairing), and their similarity of pressure distribution is quantified via a correlation coefficient. A large number (here: 100) of these frame pairs is then sorted into descending order of correlation value, and visualized as a KM curve; we build confidence limits via a bootstrap computed over 1000 replications. PLEs and the KM have robust statistical support and extensive development: the opportunities for extended application are substantial. We propose that the KM-PLE in particular, and dynamic analysis in general, may provide key leverage on future development of seating technology, and valuable new insight into extant datasets.