In this section, we describe easy methods to prepare an efficient exercise illustration mannequin that is useful for all of the recall, ranking, re-rank phases of our FSE engine. The inverse kinematics algorithm in OpenSim calculates for every time step the pose of the mannequin that greatest matches the given section orientations, whereas adhering to the predefined biomechanical constraints of the model. To achieve this, the actual information undergoes the same inverse kinematics computation because the augmented data, offering the required kinematic parameters for automated labeling. The transformation of the orientation information, as described above, solely considers particular person physique segments and does not account for the kinematic dependencies between adjacent segments. Subsequently, we define our novel augmentation method, including preprocessing of IMU data, systematic modification of movement orientations, inverse kinematics-based mostly validation, and the automatic labeling method. Since we prepare our neural networks on orientations relatively than raw IMU data, each the input and output of our augmentation course of are represented as quaternions. If the ray hits something (other part of the robot or object in the atmosphere), the output of the sensor is computed proportionally to the length of the ray.
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