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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.

Titel:	TAT-HUM: Trajectory analysis toolkit for human movements in Python.
Autor/in / Beteiligte Person:	Wang, XM ; Welsh, TN
Zeitschrift:	Behavior research methods, Jg. 56 (2024-04-01), Heft 4, S. 4103-4129
Veröffentlichung:	2010- : New York : Springer ; <i>Original Publication</i>: Austin, Tex. : Psychonomic Society, c2005-, 2024
Medientyp:	academicJournal
ISSN:	1554-3528 (electronic)
DOI:	10.3758/s13428-024-02378-4
Schlagwort:	Humans Biomechanical Phenomena Programming Languages Male Movement physiology Software
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Behav Res Methods] 2024 Apr; Vol. 56 (4), pp. 4103-4129. <i>Date of Electronic Publication: </i>2024 Mar 19. MeSH Terms: Movement* / physiology ; Software* ; Humans ; Biomechanical Phenomena ; Programming Languages ; Male References: Ayala, M. N., & Henriques, D. Y. (2021). Differential contributions of implicit and explicit learning mechanisms to various contextual cues in dual adaptation. PLoS One, 16(7), e0253948. (PMID: 342370828266054) ; Bartlett, R. (2014). Introduction to sports biomechanics: Analysing human movement patterns. Routledge. ; Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., & Grundmann, M. (2020). BlazePose: On-device Real-time Body Pose tracking. CoRR, abs/2006.10204. Retrieved from https://arxiv.org/abs/2006.10204 . Accessed 17 Oct 2022. ; Bingham, G. P., & Pagano, C. C. (1998). 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Attention, Perception, & Psychophysics, 81(7), 2410–2423. https://doi.org/10.3758/s13414-019-01821-5. (PMID: 10.3758/s13414-019-01821-5) Contributed Indexing: Keywords: Human movement analysis; Kinematic analysis; Python library; Spatial cueing paradigm; Trajectory analysis Entry Date(s): Date Created: 20240320 Date Completed: 20240529 Latest Revision: 20240530 Update Code: 20240530

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