Development of MongoDB-based Gait System with Interactive Visualization for Clinical Analysis

Gait analysis is a crucial aspect of biomechanics and medical rehabilitation, used to detect movement disorders, assess therapy effectiveness, and understand human walking patterns. In Indonesia, gait research remains limited, with most data sourced from abroad, which may not reflect the characteristics of the local population. This study uses data from Vicon camera recordings that track marker movements on the subject's body and convert them into kinematic data in spatial coordinates, stored in Excel files. To support clinical applications, an efficient system is needed to manage gait data and present analysis results interactively. Therefore, a MongoDB-based gait data management system was developed due to its flexibility in handling unstructured data and scalability. The system was designed to preprocess gait data and display the results through an interactive Streamlit dashboard. The analysis involved calculating gait angle parameters, visualized in a gait cycle angle graph and analyzed statistically using mean and standard error to improve interpretation accuracy. Testing shows that the system can store data in an average of 1.52 seconds, retrieve it in 3.598 seconds, and render visualizations in 0.192 seconds, with high accuracy and only a 0.1-degree error between the input and output. This system effectively addresses the challenge of managing local gait data and supports comprehensive biomechanical analysis, enabling clinicians to make informed decisions regarding rehabilitation needs based on deviations from normal gait angle ranges.