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

Human movement analysis Kinematic analysis Python library Spatial cueing paradigm Trajectory analysis

Journal

Behavior research methods

ISSN: 1554-3528

Titre abrégé: Behav Res Methods

Pays: United States

ID NLM: 101244316

Informations de publication

Date de publication:
19 Mar 2024

Historique:

accepted: 21 02 2024

medline: 20 3 2024

pubmed: 20 3 2024

entrez: 20 3 2024

Statut: aheadofprint

Résumé

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.

Identifiants

DOI: 10.3758/s13428-024-02378-4 PMID: 38504077

pubmed: 38504077

doi: 10.3758/s13428-024-02378-4

pii: 10.3758/s13428-024-02378-4

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

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