Interferi is an on-body gesture sensing technique using acoustic interferometry. We use ultrasonic transducers resting on the skin to create acoustic interference patterns inside the wearer’s body, which interact with anatomical features in complex, yet characteristic ways. We focus on two areas of the body with great expressive power: the hands and face. For each, we built and tested a series of worn sensor configurations, which we used to identify useful transducer arrangements and machine learning features. We created final prototypes for the hand and face, which our study results show can support eleven- and nine-class gestures sets at 93.4% and 89.0% accuracy, respectively. We also evaluated our system in four continuous tracking tasks, including smile intensity and weight estimation, which never exceed 9.5% error. We believe these results show great promise and illuminate an interesting sensing technique for HCI applications.

Research Team: Yasha Iravantchi, Yang Zhang, Evi Bernitsas, Mayank Goel, and Chris Harrison

Awards: Best Paper Honorable Mention