The quality of life is significantly affected by abnormalities in gait brought on by diseases like flat feet, Parkinson's disease, or stroke. This work presents the development of a plantar pressure monitoring system that utilizes gait analysis to aid in clinical evaluation and rehabilitation. The technology records pressure data in real-time at key plantar areas using eight Force Sensitive Resistor (FSR) sensors inserted in a mat. Reconstructing high-resolution plantar pressure maps from a small number of sensor inputs is made possible by an innovative use of compressed sensing (CS). For accurate signal reconstruction, the system employs a K-SVD-based dictionary learning framework combined with Orthogonal Matching Pursuit (OMP), where the pressure map is modeled as a sparse signal in a learned representation domain. Experimental results using a public dataset and real measurements demonstrate that the reconstructed images closely match ground truth data, with a Pearson correlation of 95.65%. This proves the feasibility of reconstructing detailed pressure distributions from sparse input data.