Elimination of undesired signals from a mixture of captured signals in body area sensing systems is studied in this paper. A series of filtering techniques including a priori and adaptive approaches are explored in detail and applied involving decomposition of signals along a new system's axis to separate the desired signals from other sources in the original data. Within the context of a case study in body area systems, a motion capture scenario is designed and the introduced signal decomposition techniques are critically evaluated and a new one is proposed. Applying the studied filtering and signal decomposition techniques demonstrates that the functional based approach outperforms the rest in reducing the effect of undesired changes in collected motion data which are due to random changes in sensors positioning. The results showed that the proposed technique reduces variations in the data for average of 94% outperforming the rest of the techniques in the case study although it will add computational complexity. Such technique helps wider adaptation of motion capture systems with less sensitivity to accurate sensor positioning; therefore, more portable body area sensing system.