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Performing laparoscopic surgery requires several skills, which have never been required for conventional open surgery. Surgeons experience difficulties in learning and mastering these techniques. Various training methods and metrics have been developed to assess and improve surgeon's operative abilities. While these training metrics are currently widely being used, skill evaluation methods are still far from being objective in the regular laparoscopic skill education. This study proposes a methodology of defining a processing model that objectively evaluates surgical movement performance in the routine laparoscopic training course. Our approach is based on the analysis of kinematic data describing the movements of surgeon's upper limbs. An ultraminiaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The data processing model was trained by using the subjects' motion features acquired from the WB-3 system and further validated to classify the expertise levels of the subjects with different laparoscopic experience. Experimental results show that the proposed methodology can be efficiently used both for quantitative assessment of surgical movement performance, and for the discrimination between expert surgeons and novices.