Ballistocardiography is a method that recently has shown potential to be- come valuable for unobtrusive cardiovascular monitoring. Ballistocardio- graphy (BCG) is the measure of displacement of the human body caused by events connected to the cardiac cycle. The BCG can be used to moni- tor cardiovascular parameters such as heart rate. Measurement of BCG can be done using multiple types of unobtrusive sensors, making it possible to create convenient monitoring systems. Although BCG has the potential to monitor multiple parameters connected to the cardiac cycle, there is a need for a better understanding of the actual BCG movement of the body, to take full advantage of this method. This thesis presents a new approach to measure and study the BCG by using optical marker-based motion capture. We provide new details about the actual movement of the body during a BCG cycle. This was achieved by facilitating an optical motion capture system, to enable acquisition of sub- millimeter movement data. Using this optimized optical motion capture setup, the OpMoBa dataset was produced for this thesis. This dataset contains data from 16 healthy individual lying on a mattress in supine position, and shows the variations of BCG movement within and across the different subjects. The dataset was subsequently used to study how the BCG signal is distributed throughout the body, and which parts of the body contained the best BCG signal, both in terms of quality and strength. Finally we studied how the BCG movement translates into movement in the different limbs of the body. This has enabled us to visualize in 3D how different body parts interact during the BCG cycle allowing Interpretation of angular and linear BCG movement. We believe that the presented motion capture setup can provided new insight into BCG signal, and that further in depth studies could reveal new potential clinical applications.