Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing


Amirreza Farnoosh, Sarah Ostadabbas, Mehrdad Nourani ;
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:13-24, 2017.


Wearable technologies can benefit from compressive sensing (CS) as an efficient signal transformation, compression, and reconstruction technique. Among such technologies, in-shoe pressure monitoring systems are designed to continuously record plantar pressure distribution for various applications ranging from medical research to product development in sports and healthcare. To gather adequate information from plantar area, a high resolution spatial pressure reading is required. However, to achieve a practical wearable monitoring system with long battery life at a reasonable price, the number of sensors in the shoe must be very limited. In this paper, we employed CS principles to reconstruct spatially-continuous plantar pressure distribution from a small number of sensors (i.e. K < 10) based on a supervised dictionary learning approach. The learned dictionary transforms the high-resolution pressure distribution to a sparse representation which is accurately reconstructable using either orthogonal matching pursuit (OMP) or least absolute shrinkage and selection operator (LASSO) algorithm. Using plantar pressure data from 5 participants, we demonstrated that our method outperforms grid-based and non-gridded interpolation techniques even at K = 4 sensors such that the best interpolation needs more than K = 170 sensors to give the same reconstruction accuracy. With K = 4 sensors, we achieved a root mean squares (RMS) reconstruction error of 6.7 kPa per sensing cell while the error remained below 16 kPa for pressure values up to 160 kPa. Our algorithm is also shown to be robust in presence of measurement error and limited training data, therefore efficiently addresses the challenges encountered in production of commercial in-shoe monitoring systems.

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