Abstract
This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers’ movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.
Highlights
Gait analysis is an important topic in recent research because it provides an effective method for healthcare and medical treatment
We propose a novel method of walking distance estimation based on a precise stride counting and phase determination using an insole sensory module that consists of a triaxial accelerometer and eight pressure sensors
Compared with the estimated moment received from the swing phase determination, we obtained the maximum difference of 0.08 s and 0.06 s for the starting and stopping points, respectively
Summary
Gait analysis is an important topic in recent research because it provides an effective method for healthcare and medical treatment. The rapid development of inertial sensor technology has enhanced data collecting methods based on human movement. Tong et al [5] used uni-axial gyroscopes attached to the leg shank and thigh segment to record angular velocity for each segment. Using these velocities, they derived the segment inclination and knee angle to estimate stride length and gait phases. Aminian et al [7] attached gyroscope sensors into each shin and on the right thigh to collect angular speed data, and estimated stride length and velocity based on wavelet analysis
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