Analysis of stride segmentation methods to identify heel strike Victoria Turchicka,b, Yulia Yatsenkoa,b, and Mark Redferna,b a b
Human Movement and Balance Laboratory, Department of Bioengineering Victoria Turchick was born and raised in Pittsburgh. Her research interests are focused on the biomechanics of human movement, and she hopes to pursue a career in the rehabilitation device industry.
Victoria Turchick
Dr. Redfern is the William Kepler Whiteford Professor in the Department of Bioengineering., with secondary appointments in the Departments of Physical Therapy, Otolaryngology, and Health and Rehabilitation Sciences. Dr. Redfern is a graduate of the University of Michigan where he earned Mark Redfern, Ph.D. his bachelor of science in engineering science and applied mechanics, and Ph.D. in bioengineering. He was a postdoctoral fellow at the University of Michigan Center for Ergonomics. Dr. Redfern’s research interests include two broad areas: the biomechanics of human movement and occupational biomechanics.
Significance statement
Three methods of stride segmentation from IMU data were compared for their ability to identify heel strike. All methods were able to accurately segment stride in all but one subject, while only two accurately identified heel strike.
Category: Methods
Keywords: inertial measurement units, stride segmentation, gait biomechanics
98 Undergraduate Research at the Swanson School of Engineering
Abstract
The purpose of this study was to compare two methods of stride segmentation to a third ‘gold standard’ method for their ability to identify heel strike from inertial measurement units (IMU). Specifically, the acceleration data from IMUs placed on the shins and the pelvis of participants were used. These data were previously collected by the University of Rome. Methods 1 and 2 used the accelerometer signals from an IMU located on the low back (which is often used in evaluating gait with IMUs), while Method 3, the ‘gold standard’, used accelerometers on the right and left shins. On average, Method 1 identified heel strike as occurring 27 milliseconds prior to heel strike identified by Method 3, and Method 2 identified heel strike 160 milliseconds prior to Method 3. Individual differences in identifying heel strike were found and the effect of performing turns compared to straight walking were also analyzed. The findings of this study show that all methods were able to accurately segment stride, but only Methods 1 and 3 were able to correctly identify heel strike in all but one subject, making them better suited for gait analysis. None of the methods were able to accurately segment stride while subjects performed turns. Future studies should explore more robust algorithms for stride segmentation to perform accurate assessments of gait from IMU data.
1. Introduction
IMU data are becoming an increasingly popular way to measure kinematics, joint angles, and analyze gait. As an alternative to traditional motion sensor systems, IMUs can operate independent of a larger in-lab system. Containing accelerometers and gyroscopes along three axes and a magnetometer, IMUs can provide three-dimensional data outside of a traditional lab environment. This allows motion data to be captured in a more natural setting. The accelerometers collect linear accelerations values in 3 orthogonal directions, the gyroscope collects angular velocity data about those same axes, and the magnetometer allows for proper orientation of collected data within Earth’s magnetic field. This study was conducted as part of a larger project to investigate ways to process IMU data that is collected during walking. A stride, or two steps, can be identified using acceleration data from these IMUs. Stride segmentation is needed to provide insight into gait symmetry, cadence, force attenuation, and individual differences in stride. By analyzing multiple steps, several strides can be analyzed and the phases within each stride can be identified. This includes heel strike, heel strike transient, toe-off, stance phase, and swing phase. The purpose of this study was to determine the differences among three methods of stride segmentation that identify the time that heel contact occurs from IMU acceleration data. This study was motivated by the observation that most current literature using IMUs to characterize gait use only one sensor, placed on the low back. Therefore, it is vital that algorithms using accelerometer data from this sensor can accurately segment stride and identify gait
