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Article: Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor
Title | Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor |
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Authors | |
Issue Date | 2022 |
Publisher | MDPI. |
Citation | Symmetry, 2022, v. 14 How to Cite? |
Abstract | The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set. |
Persistent Identifier | http://hdl.handle.net/10722/313235 |
DC Field | Value | Language |
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dc.contributor.author | Chow, DHK | - |
dc.contributor.author | Iqbal, ZA | - |
dc.contributor.author | Tremblay, L | - |
dc.contributor.author | Lam, CY | - |
dc.contributor.author | Zhao, RB | - |
dc.date.accessioned | 2022-06-06T05:48:03Z | - |
dc.date.available | 2022-06-06T05:48:03Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Symmetry, 2022, v. 14 | - |
dc.identifier.uri | http://hdl.handle.net/10722/313235 | - |
dc.description.abstract | The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set. | - |
dc.language | eng | - |
dc.publisher | MDPI. | - |
dc.relation.ispartof | Symmetry | - |
dc.title | Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor | - |
dc.type | Article | - |
dc.identifier.email | Lam, CY: lamclive@hku.hk | - |
dc.identifier.authority | Lam, CY=rp02771 | - |
dc.identifier.hkuros | 333339 | - |
dc.identifier.volume | 14 | - |