vailá: Versatile Anarcho Integrated Liberation Ánalysis in Multimodal Toolbox

Abstract

Human movement analysis is crucial in health and sports biomechanics for understanding physical performance, guiding rehabilitation, and preventing injuries. However, existing tools are often proprietary, expensive, and function as "black boxes", limiting user control and customization. This paper introduces vailá-Versatile Anarcho Integrated Liberation Ánalysis in Multimodal Toolbox-an open-source, Python-based platform designed to enhance human movement analysis by integrating data from multiple biomechanical systems. vailá supports data from diverse sources, including retroreflective motion capture systems, inertial measurement units (IMUs), markerless video capture technology, electromyography (EMG), force plates, and GPS or GNSS systems, enabling comprehensive analysis of movement patterns. Developed entirely in Python 3.11.9, which offers improved efficiency and long-term support, and featuring a straightforward installation process, vailá is accessible to users without extensive programming experience. In this paper, we also present several workflow examples that demonstrate how vailá allows the rapid processing of large batches of data, independent of the type of collection method. This flexibility is especially valuable in research scenarios where unexpected data collection challenges arise, ensuring no valuable data point is lost. We demonstrate the application of vailá in analyzing sit-to-stand movements in pediatric disability, showcasing its capability to provide deeper insights even with unexpected movement patterns. By fostering a collaborative and open environment, vailá encourages users to innovate, customize, and freely explore their analysis needs, potentially contributing to the advancement of rehabilitation strategies and performance optimization.

Figures (25)

• Figure 1: Graphical User Interface (GUI) of the vailá toolbox, built using the Tkinter library in Python. The interface is organized into three hierarchical levels. The first level consists of three main frames: File Manager (Frame A) for file operations, Multimodal Analysis (Frame B) for handling biomechanical data such as MoCap, IMU, and GNSS, and Available Tools (Frame C) for additional functionalities like visualization and file conversion. The second and third levels within each frame represent the rows and columns, where specific tools and operations are accessible. This modular structure allows for seamless data processing and analysis while providing placeholders labeled "vailá" for future tool expansions and customizations, ensuring flexibility and ease of use.
• Figure 2: A terminal session running in the Xonsh shell, displaying enhanced output with rich, and real-time debugging with ipdb. This setup allows users to mix Python 3.11.9 and shell commands while debugging and viewing formatted terminal output.
• Figure 3: Screenshot of the Get Pixel Coordinate tool within vailá. The interface allows users to navigate video frames, zoom in for precise point selection, and mark or adjust keypoints. This tool facilitates manual annotation and verification of keypoints in video data, enhancing the flexibility of the vailá toolbox.
• Figure 4: Example of a 3D animated visualization of C3D data using Plotly in vailá. The interface allows users to interact with markers, play the animation forward or backward, and adjust the display animation. These tools are located in the "Available Tools > Visualization" section.
• Figure 5: Markerless 2D processing pipeline in vailá, with each step briefly describing the operation performed.