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Journal: An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data. (Pubmed Central) - Dec 18, 2020 The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95; bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80; bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps.
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Journal: EMG-driven Forward Dynamics Simulation to Estimate in Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gait. (Pubmed Central) - Oct 2, 2019 The predicted medial, lateral, and total tibiofemoral forces represented the overall measured magnitude and temporal patterns with good root mean squared errors (RMSEs) and Pearson's correlation (?2). The model accuracy was high: medial, lateral, and total tibiofemoral contact force RMSEs = 0.15, 0.14, 0.21 body weight (BW), and (0.92< ?2<0.96) for normal gait; RMSEs = 0.18 BW, 0.21 BW, 0.29 BW, and (0.81< ?2<0.93) for smooth gait; and RMSEs = 0.21 BW, 0.22 BW, 0.33 BW, and (0.86< ?2<0.95) for bouncy gait, respectively.
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