Batch Processing

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3D Data Analyzer

1. Load MoCap (QTM TSV-Export) or
Pose/Hand Tracking Data (CSV):
(Batch processing with multiple files)
or load OpenCap/OpenSim Data (ZIP / TRC):
(Batch processing with multiple files)
1b. optional: Load Hands (MediaPipe):
1c. optional: Load Face FaceTracking:
2. optional: Load Video (MP4):
3. optional: Load 3D Avatar(s)
(Mixamo-/RPM-/GameEngine-Rig) (GLB):
optional: save edited motion data
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3D Data Analyzer © 2026 by Christoph Reuter and SInES (last modified: 6.10.2026) is released under MIT License. This tool includes movement features according to the RITMO VideoAnalysis (Jensenius 2005), Matlab MoCap Toolbox (Burger, Toiviainen 2013), PyEyesWeb (Sabharwal, Corbellini, Ghisio, Coletta, Romano, Al Foysal, Volpe & Camurri 2026) and to the PCA Movement Synergy Analysis (Bigand, Prigent, Berret, Braffort 2021)

How to cite: Reuter, C. (2026). SInES Visualizers: 3D Data Analyzer. Web Application https://sinestools.univie.ac.at/ QTMparser.htm


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Exported Movement Features (Dictionary)

Feature Name Range / Unit Description
Dist_kumuliert[0, ∞) mTotal distance traveled (cumulative) by the center of mass or the respective marker..
Speed / Acc / Jerk[0, ∞)Velocity (m/s), acceleration (m/s²), and jerk (m/s³) represent the first, second, and third mathematical derivatives of position over time.
FluidityRatio [0, ∞)The ratio of speed to acceleration. Higher values indicate smoother motion (fewer abrupt changes in speed).
Flux[0, ∞)The ratio of speed to acceleration. Higher values indicate smoother motion (fewer abrupt changes in speed).
Spread[0, ∞) mSpatial distribution. The average distance of all body markers from the current common center of mass (centroid).
BoundingBox_Vol[0, ∞) m³Volume of the smallest 3D cube that encloses the entire body posture in this frame.
KinEnergy_Win[0, ∞) JProxy for kinetic energy (velocity²) averaged over the defined sliding window (e.g., 0.5 s).
Smoothness_Win[0, 1]A measure of the “smoothness” of motion, based on jerk. 1.0 = perfectly smooth, ~0.0 = extremely jerky/abrupt.
Contraction_Win[0, ∞) m³Average bounding box volume over the time window. Shows the extent to which the body contracts or expands over time.
DirChange_Win[0, ∞) DegAccumulated changes in direction (angles in degrees) of the motion vectors over the time window. Shows a zigzag pattern.
EllipsoidSphericity[0, 1]Describes the spatial distribution of the markers (covariance matrix). 1.0 = perfectly spherical posture; close to 0.0 = extremely flat or elongated posture.
PointsDensity[0, ∞) 1/m³Number of markers divided by the bounding box volume. Indicates the compactness of the pose.
Equilibrium_Offset[0, ∞) mHorizontal distance between the center of gravity and the center of the ankles (base of support). An indicator of physical balance and forward lean.
Suddenness_Win[0, ∞)The maximum jerk within the time window. Highlights sudden, unexpected surges.
Impulsivity_Win[0, ∞)Maximum acceleration within the window, scaled by the difference between the maximum and minimum energy (amplitude).
Lightness_Win[-1, 1]Ratio of vertical velocity to total velocity. Positive values = upward (light), negative values = downward (heavy).
InterPerson_Distance[0, ∞) m(Multi-person only) The absolute physical distance between the centers of mass of two people.
InterPerson_SpreadSymmetry[0, 1](Multi-person only) Compares the “spread” (space occupied) of two people. 1.0 = exactly the same, 0.0 = extremely asymmetrical (e.g., Person 1 is tall, Person 2 is small and hunched over).
InterPerson_MovementSynchrony[-1, 1](Multi-person only) Cosine similarity of the motion vectors (centers). 1.0 = moving in exactly the same direction; -1.0 = moving in opposite directions.
Symmetry_Distance[0, 1]Measures whether the left and right joints (e.g., wrists) are the same distance from the center. 1.0 = perfect symmetry.
Symmetry_Direction[-1, 1]Cosine similarity of the velocity vectors of the left and right joint pairs. 1.0 = moving synchronously in the same direction.