Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions

Abstract

The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.

Agreement of Gait Events Detection during Treadmill Backward Walking by Kinematic Data and Inertial Motion Units

“…Our results indicated that both methods identified all gait events during BW, except for one erroneous identification of an IC (out of a total of 2861 gait events). Furthermore, the limits of agreement obtained between the two methods were consistent with previous reports that examined agreement between methods to identify gait events [13,25,26].…”

“…For example, Benson et al [13] compared IMU-based identification to a gold-standard force plate to evaluate event identification agreement during running and reported limits of agreement between −69 to 10 ms for IC and −15 to 63 ms for TC. In another study, Storm et al [26] assessed the accuracy of ankle and waist IMU sensors to detect gait events in different walking conditions.…”

“…An additional method that is extensively used for identifying stride events is based on measurements obtained from inertial motion units (IMUs) that usually comprise a gyroscope, an accelerometer, and a magnetometer [10][11][12][13]. The increasing popularity of this method may be related to the availability of these sensors, which are implemented in most modern smart cellular phones, as well as the lower cost and flexible dedicated space compared to the traditional camera-based 3D motion analysis systems.…”

Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

“…Moreover, to limit the risk of drift, the post-processing was performed in time windows around 1s to 2s [39]. On the other hand, wavelet decomposition allowed a new approach by the detection of events through pattern recognition [40][41][42] and has been tested on accelerometric signals for human locomotion [43,44]. In this study, the wavelet decomposition was adapted for horse locomotion signals and extended to post-processing of the gyroscopic signals.…”

Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters

“…Peak positive accelerations (g) along three longitudinal axes of the trunk: vertical (ACC_v), anteroposterior (ACC_ap), and mediolateral (ACC_ml), were extracted in the time domain and were defined as the maximum absolute acceleration during stance. Step detection and the contact phase was determined based on previously published algorithms (Benson et al, 2019 ).…”