Inner Cadence is an interactive video wall that captures and amplifies people’s characteristic movements. By combining machine learning with motion capture, it reveals the audience’s individual identities by enhancing motion as a form of expression.
Over the past year I have been exploring the idea of movement as a form of expression that’s inherently linked to our identity. It conveys individuality and emotion: we can be identified by our gait, or infer someone’s mood by their pose. I want to maximize this expressiveness potential and explore ways of enhancing people’s movements. I want to foreground motion by separating it from the body, silhouette, features, or clothes, making people rethink how they build their identity and their relation to others.
For this purpose I created a 3-part video wall that analyzes people’s characteristic movements, classifies and amplifies them, enhancing its expressiveness. I used motion capture hardware and machine learning algorithms to showcase expanded forms of motion which feature different textures, forms and colors. Each wall is a single-user experience, and by placing the three walls next to each other, the audience will hopefully reflect and make connections between the repeating patterns that connect them together.