Smart Insole-Based Indoor Localization System for Internet of Things Applications

With the development of Internet of Things (IoT), indoor localization has been a research focus in recent years. For inertial measurement unit (IMU)-based indoor localization method, zero velocity update (ZUPT) uses the known velocity at stationary epoch as a benchmark to calibrate the velocity drift. However, stationary epoch only takes up 24% of a whole gait cycle time, and the velocity drift at the remaining 76% time is usually estimated according to an assumption that velocity has a linear drift over time, which would introduce errors. In this paper, a two-step velocity calibration method was proposed based on human gait characteristics with Smart Insole: known velocity update (KUPT) and double-foot position calibration (DFPC). KUPT could measure the velocity from heel-strike to toe-off based on the recorded real-time foot angle and the shoe dimensions, which increases the time period when the velocity could be measured from 24% to 62% of a whole gait cycle time. DFPC method could fuse the position information of both feet based on the symmetrical characteristic of human gait to further increase the reliability of the localization results. The statistical result of a 20 times 20-m walking experiment showed that KUPT method was more accurate and reliable than ZUPT method for both feet, and DFPC method could further improve the result of KUPT method. Another experiment about walking in an indoor environment for 91 m showed that the proposed KUPT+DFPC method had an error of about 0.78 m which is acceptable for most IoT applications.

Duke Scholars

Author Ming-Chun Huang DKU Faculty