A comparison of strategies for imputing saturated pressure array data with application to the wheelchair-seating interface

Abstract

Purpose: The common responses to pressure sensor saturation are extreme: either discarding of data, or wholesale alteration of experimental protocol. Here, we test four simplistic strategies for restoring missing data due to sensor saturation, avoiding such drastic measures. Methods: We tested these algorithms on 62 pressure maps collected from 42 individuals (20 M/22 F, 54.1 ± 26.2 years, 1.7 ± 0.1 m, 71.9 ± 17.8 kg) under a variety of seating conditions. These strategies were tested via a cross-validation design, censoring the maximum pressure value in the datasets and measuring prediction error. Results: The four strategies showed various prediction error rates: ? = 0.43 ± 0.14 (simple substitution), ? = 0.16 ± 0.21 (scaled substitution), ? = 0.19 ± 0.21 (feature extraction), and ? = 0.24 ± 0.32 (extrapolation by non-linear modeling). Conclusion: For single-sensor saturation, it may be possible to restore missing data using simple techniques.

Implications for Rehabilitation

• We present a method for imputing missing data from pressure sensor arrays. The implications for rehabilitation are as follows.

• Improved flexibility in design of protocols concerning interfacial pressure measurement.

• Restoration of missing data from existing datasets.

• Reduction in recruitment burden for future studies.

• Reduction in exposure risk to study participants.

• Extrapolation
• imputation
• missing data
• pressure mapping
• wheelchair

Declaration of interest

The authors report no declarations of interest.