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Abstract
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OBJECTIVE: This work investigates real-time estimation of vertical ground reaction force (vGRF) and external knee extension moment (KEM) during single- and double-leg drop landings via wearable inertial measurement units (IMUs) and machine learning.
METHODS: A real-time, modular LSTM-based model with four sub-deep neural networks was developed to estimate the vGRF and KEM from wearable IMUs. Sixteen subjects wore eight IMUs on the chest, waist, right and left thighs, shanks, and feet and performed drop landing trials. Ground embedded force plates and an optical motion capture system were used to capture lower extremity biomechanics for model training and evaluation.
RESULTS: During single-leg drop landings, accuracy for the vGRF and KEM estimation was R(2)=0.88 ±0.12 and R(2)=0.84 ±0.14, respectively, and during double-leg drop landings, accuracy for the vGRF and KEM estimation was R(2)=0.85 ±0.11 and R(2)=0.84 ±0.12, respectively. The best vGRF and KEM estimations of the model with the optimal LSTM unit number (130) require eight IMUs placed on the eight selected locations during single-leg drop landings. During double-leg drop landings, the best estimation on a leg only needs five IMUs placed on the chest, waist, and the leg's shank, thigh, and foot.
CONCLUSION: The proposed modular LSTM-based model with optimally-configurable wearable IMUs can accurately estimate vGRF and KEM in real-time with relatively low computational cost during single- and double-leg drop landing tasks. SIGNIFICANCE: This investigation could potentially enable in-field, non-contact anterior cruciate ligament injury risk screening and intervention training programs.
Language: en |