HAND TRACKING DATA VISZUALIZER

I implemented the Mediapipe hand-tracking algorithm to enhance the interactivity of the DATA VISION process, making it more intuitive and user-friendly. The algorithm utilizes a machine learning model trained on diverse hand shapes and sizes to track hand movements frame by frame in real time. Each fingertip is represented as a district node, and I developed custom code to calculate the distance between the index and thumb nodes, dynamically determining object size. This interaction creates the effect of the user “zooming in” on a holographic object, adding a layer of simplicity and futurism to the data visualization experience.