A concurrent comparison of inertia sensor-based walking speed estimation methods

Abstract

This study performed a concurrent comparison of two walking speed estimation methods using shank- and foot-mounted inertial measurement units (IMUs). Based on the cyclic gait pattern of the stance leg during walking, data was segmented into a series of individual stride cycles. The angular velocity and linear accelerations of the shank and foot over each of these cycles were then integrated to determine the walking speed. The evaluation was performed on 10 healthy subjects during treadmill walking where known treadmill speeds were compared with the estimated walking speeds under normal and toe-out walking conditions. Results from the shank-mounted IMU sensor yielded more accurate walking speed estimates, with a maximum root mean square estimation error (RMSE) of 0.09 m/s in normal walking and 0.10 m/s in toe-out conditions; while the foot-mounted IMU sensors yielded a maximum RMSE of 0.14 m/s in normal walking and 0.26 m/s in toe-out conditions. Shank-mounted IMU sensors may prove to be of great benefit in accurately estimating walking speeds in patients whose gait is characterized by abnormal foot motions.

Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data

“…Only a few techniques not involving the use of a biomechanical foot model have been explored to avoid increased drift errors during walking [ 5 , 16 , 17 , 18 , 19 , 20 ]. Such a method has been applied to estimate maximum foot clearance values from double integration of acceleration data [ 16 ].…”

“…According to another proposed ZVU-based correction method [ 19 , 20 ], the sensor error was modeled as a constant acceleration bias in the sensor coordinate system and corrected backwards based on the estimated error observed at each detected event. In these two studies, ZVU-based correction was applied to estimate driftless velocities and thereby calculate the walking speed over the stride.…”

“…In the present study, we focused on the double integration of foot acceleration data from a foot-mounted IMU. We therefore used the ZVU principle to limit the increase in integration drift between two successive strides and then applied the above-mentioned acceleration correction method [ 19 , 20 ] to each stride. For displacement estimation purposes (particularly foot clearance estimation), we adapted the same principle to vertical velocity integration and correction, by generalizing to a zero displacement update (ZDU) principle, where walking is assumed as occurring on flat and horizontal ground.…”

Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

“…We started from the algorithm originally proposed by Li et al [17] and improved later by Laudanski et al [18] which computes the walking speed from a shank-mounted IMU. We adapted it for calculating the stride length, by correcting and integrating the linear velocity along the stride duration, thereby obtaining the length of each stride.…”

IMU based detection of freezing of gait and festination in Parkinson's disease

“…Gait segmentation and stride length performed using inertia sensor-based walking methods [13]. Using gyroscopic data for str the horizontal speed is calculated from the ve the stride onset and offset (gyroscopic signa the accelerometric data integration.…”