47 lines (36 with data), 2.1 kB
Main.ModelSetup.LabSpecificData = {
// Switch to enable Inertial MoCap functionality
#path MOCAP_FORCE_PLATE_FILE "ForcePlates.any"
#path MOCAP_BVHSETTINGS "BVHSettings.any"
#path BODY_MODEL_CONFIG_FILE "BodyModelConfig.any"
#path MOCAP_MARKER_PROTOCOL_FILE "<ANYMOCAP_PATH>/Protocols/XsensBVH.any"
#path MOCAP_EXTRA_DRIVERS_FILE "ExtraDrivers.any"
#define MOCAP_INPUT_DATA_TYPE "BVH"
#define MOCAP_C3D_DATA_PATH "../BVH-files"
#define MOCAP_USE_GRF_PREDICTION ON
#define MOCAP_FILTER_JOINT_ANGLES ON
// This model includes a Box object which is connected at both hands. The experimental
// data (.BVH files) does not include information about the box. What we do is to model
// the box obejct with the correction dimensions and mass properties. We let the human
// move the Box and track the kinematics of the box during the markertracking study.
// For simplicity we constrain the box from rotating. The position and orientation of the
// box are saved with other joint angles as input to a driver during the inverse dynamics study.
Main = {EnvironmentModel = {#include "box.any"};};
// GRF prediction models run with more numerical stability using a second order
// criterion. This is due to the big difference in strength of recruited actuators
// (artificial muscles) which is used as contract element under the feet
// (high strength) and human-ground residuals (low strength)
Main.Studies.InverseDynamicStudy.InverseDynamics.Criterion.Power = 2;
Gravity = -9.81*{0,1,0};
LowPassFilterSettings = {
MarkerFilterCutOffFrequency = 5;
};
// This calculates the segment dimensions from the BVH rig
// The will have to be modified if a different type of BVH
// file is used.
#include "CalculateSegmentDimensionsFromBVH.any"
// Include functions to generate a video:
// -> Run `Study.VideoTool.Preview` to preview the camera view.
// -> Run `Study.VideoTool.Create_Video` to generate video from the analysis.
#include "CreateVideo.any"
};