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- W4387062893 abstract "Ground reaction force (GRF) is an important parameter for biomechanical analyses in running. For example, GRFs are used as input for inversed dynamics to estimate joint load or powers, which can give insights in the aetiology of running injuries. However, GRF measurement is restricted to the laboratory setting. Machine learning methods have been used to estimate GRF from inertial measurement units (IMUs), but creating robust generic models is challenging. An ensembled model, where multiple models are combined to create an estimate, could enhance the performance of (3D) GRF estimation models. PURPOSE: Ensemble multiple models to improve the overall performance to estimate 3D GRFs in running. METHODS: 11 experienced heel strike runners (4 F, 7 M, 30.6 ± 8.3 years, 1.79 ± 0.11 m, 74.2 ± 17.4 kg) ran 9 trials on a force-instrumented treadmill at a combination of three velocities (10, 12 and 14 km/h) and stride frequencies (preferred, ±10%). Subjects were instrumented with IMUs (240 Hz) at both proximal tibias and pelvis. Using leave-one-subject-out cross validation, artificial neural networks (two layers, both 250 neurons) were trained to estimate 3D GRF where the remaining subjects were randomly assigned to the training (n = 6) or validation (n = 4) set to create 7 different models. The ensembled model averaged the estimate of the 7 different ensemble members. For each subject, the ensembled model is compared to the 7 ensemble members in terms of relative root mean squared error (rRMSE), which is the RMSE normalized by the full range of the measured forces in that direction, multiplied by 100. RESULTS: The ensembled estimates performed better than the average performance over the ensemble members (Table 1). Furthermore, for 21 out of 33 GRF estimates, the ensembled model had similar or better performance than the best ensemble member. CONCLUSION: An ensembled model increases the performance of machine learning models to estimate 3D GRFs in running based on IMU data." @default.
- W4387062893 created "2023-09-27" @default.
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- W4387062893 date "2023-09-01" @default.
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- W4387062893 title "Ensembled Models Improve The Accuracy Of IMU Based 3d Ground Reaction Forces Estimation" @default.
- W4387062893 doi "https://doi.org/10.1249/01.mss.0000983824.63971.54" @default.
- W4387062893 hasPublicationYear "2023" @default.
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