One Leg Stance (OLS), a test assessing postural stability, is popularly conducted both in clinic and community settings because it is inexpensive and time-efficient. However, the evaluation based on visual observation and manual time measurement with a stop-watch cannot provide quantitative and detailed parameters for longitudinal or cross-sectional studies. In recent years, to overcome these limitations, the use of Inertial Measurement Unit (IMU) as objective measurement analysis tools is becoming more and more popular. However, the greatest issue is that IMU data segmentation is still time-consuming and prone to errors, as the OLS segmentation is being done manually, off-line, on recorded data. In this paper we proposed a novel algorithm for the automatic segmentation of IMU data of the OLS test. The result showed that the correct rate of detection was over 90% which was close to the correct rate in manual segmentation. Compared to manual segmentation with video, besides being less time-consuming, the proposed algorithm closes the loop making the data acquisition and analysis completely automatic, thus can be integrated in self-assessment smart phone applications, allowing the continuous tracking of postural stability also outside clinics and health-care facilities.