GANspire is a deep learning tool that generates expressive breathing waveforms for art and health applications.
Our design intention for GANspire is threefold. First, we wanted to create a deep generative model that captures fine-grained, expressive variations of breathing in humans. Second, we wished to design a tool that facilitates exploration of the generative model for non-expert users. Third, we sought to apply our tool to explore breathing as a new form of creative output—specifically, as part of the RÉESPIRATION art-health research project.
We are currently leading participatory design to develop such a deep learning tool, that we named GANspire. We crafted a deep learning prototype by training a WaveGAN over a small dataset of breathing pressure waveforms sampled in physiology studies. We extracted interpretable controls from the breathing latent space using GANSpace. We developed a parametric interface for exploration of breathing generation using the Marcelle web-based toolkit. We currently collaborate with pulmonologists to identify and tackle socio-material issues emerging from the design of our tool.
The project is being developed with Baptiste Caramiaux, Thomas Similowski, and Samuel Bianchini in collaboration with the HCI Sorbonne group of ISIR, the R3S department of AP-HP, and the Reflective Interaction group of EnsadLab, in the context of a postdoctoral fellowship program at Faculty of Medicine, Sorbonne Université.
Paper at NeurIPS 2021