TAT-HUM: Trajectory Analysis Toolkit for Human Movements in Python
Bibliographic record
Abstract
Behavioral research relies on evaluating measurable behaviours to extract their underlying social, cognitive, and neurophysiological mechanisms. Although traditional methods commonly involve simple measures of discrete movements (e.g., RT of keypresses), analyses of dynamic human movement patterns have shown to reveal additional insights. Effectively extracting information from movement trajectories patterns can be challenging because of the complex and dynamic nature of the movements. The current presentation outlines a custom Python toolkit for analyzing human manual movements and extracting relevant information. This toolkit can process single discrete rapid aiming movements in two- (e.g., cursor pointing) and three-dimensional (e.g., manual pointing) space, as well as cyclical movements (e.g., Fitts’s Law task). This toolkit uses various Python libraries, including NumPy and SciPy, and provides a set of frequently used functions for analyzing movement trajectory data. To ensure versatility and user-friendliness, the toolkit offers two approaches: an automated method that processes raw data and generates relevant measurements without intervention, and a manual approach that allows users to selectively utilize different functions according to their specific requirements. The results of a behavioral experiment based on the spatial cueing paradigm was conducted and will be reported to demonstrate the practical application of this toolkit. Readers are encouraged to access the publicly available data and analysis scripts to gain insight into kinematic analysis for human movements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.031 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".