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, could be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract useful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit utilizes various open-source Python libraries, such as NumPy and SciPy, and offers a collection of commonly used functions to analyze movement trajectory data. To ensure flexibility and ease of use, this 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 publically available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.