Product limit estimation for capturing of pressure distribution dynamics

•First dynamic analysis for wheelchair seated interface pressure data.•Analysis is based on the Kaplan–Meier Product Limit Estimator.•Dynamic pressure distribution data analysis may be key for development of seating technology in the future.•Provides new insight into existing data sets using seat interface pressure under dynamic conditions.

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.