Smartwatches and wearables are unique in that they reside on the body, presenting great potential for always-available input and interaction. Their position on the wrist makes them ideal for capturing bio-acoustic signals. We developed a custom smartwatch kernel that boosts the sampling rate of a smartwatch’s existing accelerometer to 4 kHz. Using this new source of high-fidelity data, we uncovered a wide range of applications. For example, we can use bio-acoustic data to classify hand gestures such as flicks, claps, scratches, and taps, which combine with on-device motion tracking to create a wide range of expressive input modalities. Bio-acoustic sensing can also detect the vibrations of grasped mechanical or motor-powered objects, enabling passive object recognition that can augment everyday experiences with context-aware functionality. Finally, we can generate structured vibrations using a transducer, and show that data can be transmitted through the human body. Overall, our contributions unlock user interface techniques that previously relied on special-purpose and/or cumbersome instrumentation, making such interactions considerably more feasible for inclusion in future consumer devices.
Gierad Laput, Robert Xiao, and Chris Harrison. 2016. ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16). ACM, New York, NY, USA, 321-333. DOI: http://dx.doi.org/10.1145/2984511.2984582