Accurate Estimation of Running Temporal Parameters Using Foot-Worn Inertial Sensors

Abstract

The aim of this study was to assess the performance of different kinematic features measured by foot-worn inertial sensors for detecting running gait temporal events (e.g., initial contact, terminal contact) in order to estimate inner-stride phases duration (e.g., contact time, flight time, swing time, step time). Forty-one healthy adults ran multiple trials on an instrumented treadmill while wearing one inertial measurement unit on the dorsum of each foot. Different algorithms for the detection of initial contact and terminal contact were proposed, evaluated and compared with a reference-threshold on the vertical ground reaction force. The minimum of the pitch angular velocity within the first and second half of a mid-swing to mid-swing cycle were identified as the most precise features for initial and terminal contact detection with an inter-trial median ± IQR precision of 2 ± 1 ms and 4 ± 2 ms respectively. Using these initial and terminal contact features, this study showed that the ground contact time, flight time, step and swing time can be estimated with an inter-trial median ± IQR bias less than 12 ± 10 ms and the a precision less than 4 ± 3 ms. Finally, this study showed that the running speed can significantly affect the biases of the estimations, suggesting that a speed-dependent correction should be applied to improve the system’s accuracy.

Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy

“…To guaranty reproducible measure and be independent of the IMU location on each segment, lower limbs sensors were automatically aligned with the functional axis of the movement. To this end, assuming that the main angular rotation during gait occurs around the medio-lateral axis of each segment, principal component analysis (PCA) was applied on angular velocity to assess the pitch component of the shanks and thighs rotation 44,45 . For each trial, the norm of acceleration of the chest was computed, to preclude wrong axis selection resulting from potential misalignment of the sensor with regard to the chest.…”

Drift-Free Foot Orientation Estimation in Running Using Wearable IMU

“…Note that there was no sensor located on the forefoot segment. We aligned the IMU's technical frame (TF) with the rearfoot functional frame (FF rear ), as described by Falbriard et al (2018); we recorded a standing period and used the gravitational acceleration to set the FF rear y-axis parallel to the vertical axis of the foot. Then, using principal component analysis (PCA) on the running measurements, we aligned the FF rear z-axis with the principal vector, which we assumed parallel to the mediolateral axis of the foot.…”

“…Temporal events detection was based on previously validated algorithms (Falbriard et al, 2018). We segmented the trials into running strides and extracted four events per stance phase.…”

On-field player workload exposure and knee injury risk monitoring via deep learning

“…To validate the model by comparing calculated and predicted KJM over a standardized stance phase, it was necessary for both the training and test sets to include gait event information defined from a common source, which in this instance was the force plate. This requirement would not extend to real-world use, where it is envisaged that event data would be predicted from a continuous stream of input kinematics from wearable sensors located on the feet [21]. For this study, foot-strike (FS) was automatically detected by foot position being within the force plate corners, and if calibrated F z was continuously above the threshold (20 N ) for a defined period (0.025 s); toe-off (TO) by F z falling below a second threshold (10 N ) [36,37,51].…”