Plantar pressure measurement transformation framework

Abstract

Pedobarography measurements of pressure distribution across the plantar surface can be a source of valuable information for gait analysis in context of injury prevention, improvement in balance control, diagnosing disease, and gait analysis. Different applications demand different measurement types: platforms in the gait labs or in-shoe smart soles, and subsequent analysis methods vary with the application domain. Although the pedobarography is considered experimental, technology advancements in the field of IoT, and popularity of collecting different data linked to human activities and behavior, are contributing to increase in pedobarography research. Comprehensive analysis of research results is impeded since the data collecting is not standardized, and differ in volume and structure, thus not facilitating comparative analyses, as it is a case with other biomedical signals as ECG or EEG. In our research we have implemented software framework in Python language with objective to extract relevant pedobarography information using foot segmentation and data aggregation algorithms. In order to validate our solution we processed data from public plantar pressure data set and transformed it in data sets comprising pressure signals covering selected number of foot segments, down to one sensor signal. The proposed data transformation application can help in data sharing and comparison of different approaches in pedobarography.