TAT-HUM: Trajectory analysis toolkit for human movements in Python

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help use...

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Vydáno v:Behavior research methods Ročník 56; číslo 4; s. 4103 - 4129
Hlavní autoři: Wang, Xiaoye Michael, Welsh, Timothy N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2024
Springer Nature B.V
Témata:
Accuracy
Behavior
Behavioral Science and Psychology
Biomechanical Phenomena
Cognition & reasoning
Cognitive Psychology
Cues
Data
Decision making
Humans
Information processing
Kinematics
Libraries
Male
Movement - physiology
Original Manuscript
Programming Languages
Psychology
Python
Scripts
Software
Spatial cueing paradigm
Kinematic analysis
Human movement analysis
Trajectory analysis
Python library
ISSN:1554-3528, 1554-3528
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Shrnutí:Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source Python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.
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ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-024-02378-4