Algorithm for Gait Parameters Estimation Based on Heel-Mounted Inertial Sensors

Gait analysis serves as a pivotal tool in identifying abnormalities associated with various disorders. Recently, inertial measurement units (IMUs) have emerged as a feasible tool, showing promising results for continuous gait monitoring. However, current gait analysis algorithms often overlook the importance of sensor placement and corresponding motion characteristics. Moreover, there has been limited effort to tailor gait analysis algorithms for optimal performance with sensors placed in specific locations. In response to this, we propose a novel gait analysis algorithm designed for heel-mounted IMUs. Our algorithm employs refined methods to accurately assess heel dynamics and calculate a comprehensive range of spatiotemporal gait parameters and parameters related to symmetry and variability. Experiments with straight walking and daily activity simulation were performed and an optical motion capture (OMC) system was used as a reference system. The results demonstrated strong correlation (
${r} \gt 0.9$
) and good agreement with common gait parameters even in daily conditions (stride length
$- 0.009~\pm ~0.055$
m, stride time
$- 0.002~\pm ~0.023$
s, and walking speed
$- 0.004~\pm ~0.048$
m/s). All spatiotemporal gait parameters exhibit high reliability, as indicated by a minimum intraclass correlation coefficient (ICC) of 0.921. The findings affirm the potential of the proposed algorithm to perform daily gait analysis and monitoring task, offering a reliable tool for professionals in the field. By addressing the shortcomings of existing algorithms and focusing on the heel, our approach contributes to the advancement of gait analysis, paving the way for more accessible and accurate gait assessment methods in real life.