Measurement plays an essential role in both production and daily life. With ongoing advancements in Inertial Measurement Units (IMUs) based on Micro-Electro-Mechanical Systems (MEMS), combined with Strapdown Inertial Navigation algorithms, the development of measurement devices has the potential to overcome the spatial limitations of conventional measurement tools. The primary advantage of inertial measurement systems lies in their ability to estimate their own position during motion, thereby preserving trajectories and enabling the extraction of distance information from these paths. However, low-cost MEMS IMUs are prone to considerable measurement error, as they introduce substantial noise and accumulate error over time, leading to reduced measurement accuracy. To address this issue, a linear acceleration sequence processing method based on two-level segmentation and standard deviation analysis is proposed. This method retains linear acceleration data as sequential information and minimizes error through segmentation, interpolation, and statistical techniques. An experimental validation platform built with low-cost MEMS inertial devices has demonstrated the feasibility of MEMS IMU-based measurements, with the proposed method improving short-term inertial navigation measurement accuracy on this platform.