On this part, we describe easy methods to practice an effective exercise representation mannequin that is useful for all of the recall, ranking, re-rank stages of our FSE engine. The inverse kinematics algorithm in OpenSim calculates for each time step the pose of the model that best matches the given segment orientations, whereas adhering to the predefined biomechanical constraints of the model. To achieve this, the actual knowledge undergoes the same inverse kinematics computation as the augmented information, offering the mandatory kinematic parameters for automated labeling. The transformation of the orientation data, as described above, only considers individual body segments and does not account for the kinematic dependencies between adjacent segments. Subsequently, we define our novel augmentation method, including preprocessing of IMU information, systematic modification of motion orientations, inverse kinematics-based mostly validation, and the automated labeling method. Since we train our neural networks on orientations relatively than uncooked IMU information, each the enter and output of our augmentation course of are represented as quaternions. If the ray hits something (other part of the robotic or object within the surroundings), the output of the sensor is computed proportionally to the size of the ray.
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