Open-source web application for motor learning research

The motivation for this project was to address significant barriers in procedural skill learning research. Traditional motor learning experiments require controlled lab environments, which limit sample size, accessibility, and diversity. Inspired by the need for more inclusive and scalable research methods, I developed an open-source online platform. This platform democratizes access to motor learning studies by removing coding prerequisites and simplifying experimental setup, making cutting-edge research feasible for a broader audience.

To achieve this, I used Django for backend development and Vue.js for frontend design. Currently hosted on Google Cloud (available here), the platform offers a user-friendly interface for researchers to create, publish, and manage sequential finger-tapping experiments. Participants can engage from any location using standard devices, and researchers can access pre-processed data instantly. Features include real-time feedback, customizable trials, and easy data management, demonstrating my capability to integrate robust backend processing with intuitive user experiences.

We conducted two key experiments to validate the platform. I collected data from 43 participants—18 supervised in a controlled lab environment and 25 unsupervised at home. The results showed no significant differences between the supervised and unsupervised groups, confirming that the platform reliably produces comparable results regardless of the testing environment. Additionally, we successfully replicated findings from a previous study on motor learning, demonstrating the platform’s ability to yield scientifically valid outcomes. These experiments highlighted the robustness and versatility of the platform for procedural skill research.