관성 센서 데이터를 사용하여 지면 반력을 추정하고 생체역학적 변수를 도출할 수 있다.
The brain-skull interface exhibits different mechanical behavior under tension and compression, and modeling it as a rigid connection or frictionless sliding contact may not accurately represent its behavior.
Proposing a novel biomechanics-aware network for accurate 3D kinematics estimation using synthetic data, outperforming existing methods.
Learning constitutive laws and microstructure of biotissues using Heterogeneous Peridynamic Neural Operators.
Developing a novel neural network architecture enforcing constitutive constraints improves accuracy in multiscale modeling of biological tissues.
提案された新しいバイオメカニクスに配慮したネットワークは、合成データのみで訓練され、複数のデータセットを通じて最先端の手法を上回る性能を示しました。
This study compares human walking biomechanics on solid ground and sand, revealing significant differences in gait patterns and joint mechanics between the two terrains.
The author proposes a novel biomechanics-aware network that directly outputs 3D kinematics from two input views, trained on synthetic data, outperforming previous methods across multiple datasets.
The author proposes a method for enhancing biomechanical simulations using dual weighted residual-driven adaptive mesh refinement, targeting a user-defined quantity of interest.