Locomotive syndrome (LS) is a condition in which a person's mobility is impaired due to musculoskeletal disorders caused by aging or lack of exercise, signaling an imminent need for primary nursing care. This study aims to develop a smartphone application that can distinguish the LS stage in a safe, simple, and quantifiable manner. Using a six-axes accelerometer built into the smartphone, acceleration and angular velocity data were collected from elderly subjects performing alternating one-legged stances at intervals of 2.66 s. Frequency analysis was then performed with a fast Fourier transform (FFT) and an autocorrelation coefficient to compare the data characteristics of subjects with and without LS. Due to huge individual variances, significant differences were not observed between LS and non-LS, and discriminating the LS stage using frequency analysis was considered difficult in the present study. In contrast, the autocorrelation coefficient showed significant differences between LS and non-LS in acceleration and angular velocity in specific directions. Using the autocorrelation coefficient of acceleration and angular velocity data to detect the presence of LS was concluded to be effective.