Fluid accumulation in the human body is, in many cases, a symptom of some underlying pathological condition. Most common examples include edema, ascites, pleural effusion, and internal bleeding. Currently available non-invasive methods to assess fluid accumulation in the body are mainly imaging-based (i.e. ultrasound, computed tomography). This means that monitoring is limited to spot-checks and is only performed when the presence of a particular condition is already suspected from prior clinical evaluation of the patient. There is high clinical interest in the development of a system allowing such monitoring to be performed automatically, continuously over a prolonged period of time, and independently of prior clinical evaluation. For instance, in the case of internal bleeding, hemorrhage indicators such as variation in heart rate and blood pressure are only observable after significant blood loss (up to more than 1 liter) has already taken place, therefore an early warning system can save lives, decrease hospital stay length and significantly reduce complication-related costs. In this thesis, a pioneer solution is proposed for development of such a monitoring system. The hypothesized solution is based on the observation of specific energy-describing features of the Ballistocardiogram (BCG) signal, a measure of the periodic displacements generated on the body as a result of ballistic forces produced by the heart during each cardiac cycle. Because of additional damping generated by the presence of internally accumulated fluid, the energy of this signal is expected to decrease compared to its baseline value. The BCG can be recorded unobtrusively with different sensing modalities on the surface of the body. In order to validate the research hypothesis and assess the feasibility of this novel technique, a custom experimental set-up exploiting several different BCG sensing options has been used to carry out a study on 15 human volunteers. Localized fluid accumulation along the GI tract was induced in a controlled, safe and simple fashion, by means of water intake by the participants, and the BCG signal was recorded before and after intake. Signal feature exploration and performance analysis has been used to develop an optimized feature able to accurately capture the decrease in signal energy due to fluid accumulation, as well as to identify the most suitable sensor type and monitoring body location for the present application. The developed energy-describing feature shows a significant decrease in energy value from baseline to after-intake condition with a p-value<0.001. Moreover, the selected feature was able to correctly identify presence of fluid accumulation with high sensitivity (90% in bed-based, and 100% in standing-position monitoring). Given the promising results of the present study, further research towards improvement and development of the proposed technique is highly encouraged.