This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree of freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically-diagnosed mTBI. For this dataset, 6DOF criteria were most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.

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Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present Motion Planning Networks (MPNet), a neural network-based novel planning algorithm. The proposed method encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations. We evaluate MPNet on various 2D and 3D environments including the planning of a 7 DOF Baxter robot manipulator. The results show that MPNet is not only consistently computationally efficient in all environments but also generalizes to completely unseen environments. The results also show that the computation time of MPNet consistently remains less than 1 second in all presented experiments, which is significantly lower than existing state-of-the-art motion planning algorithms.

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