The quadriceps muscle plays an essential role in locomotion and knee stability, yet conventional methods for muscle force estimation are often bulky, expensive, and invasive, limiting their suitability for continuous and scalable measurements. To address this limitation, we present a modeling and simulation framework that integrates a FlexiForce A502 thin-film sensor with a pneumatic artificial muscle (PAM) to emulate quadriceps-like force behavior. The proposed model establishes the relationship between internal chamber pressure and localized surface force, while the dynamic response of the thin-film sensor is characterized using a first-order differential equation. Experimental data were analyzed through curve fitting and parameter identification in MATLAB, yielding strong agreement between measured and simulated signals, with a coefficient of determination (R2) of 0.934 and a root mean square error (RMSE) of 0.1493V, thereby validating the proposed modeling approach. A corresponding system-level simulation implemented in Simulink demonstrated that PAM input pressures ranging from 120 to 600 kPa can generate localized patch forces of up to 450 N. These results verify the feasibility of thin-film sensors for real-time muscle-like force estimation and highlight their potential applications in biomechanics, rehabilitation, and soft robotic systems.