Sensie Real-time biofeedback and mindfulness
Visualizing the invisible: A biometric-driven motion system
Sensie is a mental health platform that uses biometric data to help users manage stress and improve their emotional well-being. Working with Z1, the project focused on creating a bridge between physiological data and visual feedback. My role was to design a system of interactive animations that translate stress measurements into a fluid, visual experience, allowing users to interact with their own biometric state through guided exercises.
Rhythmic wave patterns for stress management
The core of the experience is built around interactive wave animations that react to the user’s stress levels and breathing patterns. I designed these waves to function as a visual anchor during meditation sessions, where the undulation rhythm guides the user’s breath. The work involved creating a balanced motion language—calming yet responsive—that synchronizes with the app’s sensors to provide a tangible, visual representation of the user’s internal state.
Next case study
