3D-Foot Plantar Pressure Reconstruction based on the IEE Foot Smart Insole

Within the growing technology nowadays, the study and research in the human foot have also become much more important. Advanced dynamic foot plantar pressure monitoring applications becomes useful in many healthcare fields, e.g. podiatric and orthopedic applications, rehabilitation tools, sports and fitness training tools. The new IEE1 High- Dynamic (HD) 8-multicells smart sensor provides a single insole-solution for daily usage in order to acquire information on the plantar load distribution for health prophylaxis in a large range of different shoe configurations in real time. Depending on the tracked features, 4, 8 or more sensing cells may be necessary to pick the relevant pressure information. However a high number of cells implies powerful read-out electronics, which in turn implies power consumption challenges and might lead to customer dissatisfaction similarly to the first generation of Apple Smart watch. Knowledge should be built up on the way to get from limited number of cells as relevant information as with a high-resolution sensor. This could be very challenging, because every human has a different unique pressure map, i.e. more phenomenon concentrating in some foot zone location than other person. For example, trying to determine the size and shape of pressure peaks, might require a cluster of samples, whereas the relatively flat surface of the surrounding plain might require only a few. Sophisticated mathematical models will be used to generate the complete high-resolution pressure distribution (HRPD) on each foot based on spatial interpolation schemes. The paper is organized as follows, in Section I we provide an overview of challenges and opportunities around the reconstruction of the 3D Foot Plantar Pressure (FPP). Then in Section II we underlying background needed to understand the human generic gait and describe the new smart insole designed by IEE. In Section III, we develop and apply the spatial interpolation model (SIM) to our underlying problem. Next we discuss and present in Section IV the estimated pressure map based on 3 different approaches, followed by a comparison and validation of their efficiency, reliability and accuracy. In Section V, we use mathematical optimization methods (MOM), e.g. the Particle Swarm Optimization (PSO), in order to determine the optimal location, as well the number of sensors cells needed on the relevant foot pressure information. Finally, Section VI gives the concluding remarks and future work in this topic