The Functional Movement Screen (FMS) is a useful tool to assess functional abilities in a pre-participation screening. Its seven dynamic movement tests reveal shortcomings in stability and mobility and screen the whole body. However, the current test protocol delivers results that are subjective, qualitative and have to be manually processed. This article presents a semi-automatic system to overcome these limitations for the Deep Squat test. The system consists of four wireless inertial sensors and a central Android-based processing node for data analysis and result storage. We developed our system based on data from ten subjects and evaluated the results with the FMS scoring guidelines. The sensor-based scoring system completely agreed with the manual scoring in eight out of ten subjects. In addition, quantitative information in case of compensation movements was logged. Thus, our system is capable of simplifying the FMS test and enhances the score with objective, quantitive and automatic results.