The evaluation of Anthropomorphic Test Device (ATD) kinematics, particularly head trajectory, presents a significant challenge in aerospace seat development and certification testing. Optical motion tracking is a widely used method for tracking and plotting trajectories during aircraft crash tests. It has however, several inherent limitations. While recent advancements in software have enhanced the accuracy of post-processing, maintaining continuous target visibility remains a challenge. This study focuses on development of an approach for tracking the head trajectory of an ATD for better understanding of ATD kinematics during crash tests. While sensor-based tracking is a more time and cost-effective method than optical motion tracking, previous studies have indicated that it might at times provide lower accuracy. The primary objectives of this research are to investigate and improve the accuracy of sensor-based tracking, to develop a methodology to capture it, and to compare the results for accurate and better prediction. The proposed methodology relies on a MATLAB-based algorithm consisting of spatial transformation matrix operations with Euler parameters, numerical methods and integration techniques. It utilizes initial ATD position measurements, angular velocity and acceleration data from the sensors, as inputs. It is capable of generating the 3D trajectory of the head. The potential for use of this methodology to the ATD chest is also investigated. It shows promising advancement in sensor-based tracking, suggesting that with the development of proper MEMS technology, sensor-based tracking could potentially replace optical motion tracking. For further studies, there is a wide possibility of extending the research to other body parts like lower extremities of the body to get holistic understanding of kinematic behavior