This study is aimed at exploring a technology that can use the human physiological information, such as Force Myography (FMG) signals to provide sensory feedback to prosthetic hand users. This is based on the principle that with the intent to move the prosthetic hand, the existing limbs in the arm recruit specific group of muscles. These muscles react with a change in the cross-sectional area; piezoelectric sensors placed on these muscles will generate a voltage (FMG signals), in response to the change in muscle volume. The correlation between the amplitude of the FMG signals and intensity of pressure on fingertips during grasping is then computed and a dynamic relation (model) is established through system identification in MATLAB. The estimated models generated a fitting accuracy of more than 80%. The model is then programmed into the Arduino microcontroller, so that a real-time and proportional force feedback is channeled to amputees through a micro actuator. Obtaining such percentages of accuracy in sensory feedback without relying on touch sensors on the prosthetic hand that could be affected by mechanical wear and other interaction factors is promising. Applying advanced signal processing and classification techniques may also refine the findings to better capture and correlate the force variations with the sensory feedback.