Motor Skill Acquisition Error Measurement System
Joshua R. Samson, Michael B. Cabrera, Milton Tinoco
School of Engineering
Faculty Supervisor: Alyssa Kubota
This project presents the development of a free, open-source Python-based tool for measuring error distances in motor skill acquisition research, specifically addressing the needs of the Skill Acquisition and Neuroscience Lab at San Francisco State University. The system improves upon an existing paid, LabView-based solution by streamlining workflows, reducing manual inputs, and integrating enhanced features. Employing a PyQt5-based Graphical User Interface (GUI), the tool enables precise user interactions, including zooming and selectable axes, to accurately identify puck positions in an adapted shuffleboard task. By setting an easy setup for the calibration, scaling, and data processing, the program significantly reduces errors, rework, and user frustration. The resulting solution offers a user-friendly, cost-effective, and flexible platform that researchers can adapt and expand upon, ultimately advancing the study of motor skill acquisition.