An Improved Method of Step Length Estimation with Inertial Sensors

This paper addresses reliable and accurate step length estimation using inertial sensors. Step length is an important parameter for the accurate position required in the location and navigation system. To tackle the challenges of drifting in accelerometer, sensitivity to user physical characteristics and walking profiles, as well as variability in environment, we have developed a calibration algorithm for reliable detection of gait parameters including step number, step frequency, maximum and minimum of acceleration magnitude. We've built a Radial-basis Function (RBF) neural network to train the model of step length that can adapt to different users. The established mathematical model can achieve the simple and efficient estimation of step length in realtime system. Extensive experiments have been conducted on 5 subjects with 3263 steps testing in total. Evaluation results showed our improved step length estimation method can achieve the recognition rate for step detection of 96% and a mean error of 0.04m for the step length estimation.