With the recent development of high field MRI scanners, the risk for healthcare staff being exposed to large static magnetic fields (3T to 7T) and rapidly time-varying magnetic field gradients is greatly increased. A better understanding of the interaction mechanisms and the bio-effects associated with MRI environment would allow sensible and workable exposure limits to be set for staff, patients and volunteers. This paper presents a novel approach in modeling hazardous electric field levels induced in a human body under continuous movements within a strong magnetic field environment. The derived algorithm is able to accurately model both translational motion and rotating body movements. Since this algorithm is based on the quasi-static Finite-Difference approximation, the computational space for modeling a human body can then be divided into a large number of cubic cells. Every cell in the model is very suitable for parallelization and hardware acceleration using General Purpose Graphical Processing Units (GPGPU). After adopting several optimization techniques, a speedup of around 40 times is achieved by adopting GPGPU for modeling torso movements around 8 million cells compared with a CPU implementation.