Less Is More

Abstract

Back-of-device (BoD) interaction using current smartphone sensors (e.g. accelerometer, microphone, or gyroscope) has recently emerged as a promising novel input modality. Researchers have used a different number of features derived from these commodity sensors, however it is unclear what sensors and which features would allow for practical use, since not all sensor measurements have an equal value for detecting BoD interactions reliably and efficiently. In this paper, we primarily focus on constructing and selecting a subset of features that is a good predictor of BoD tap-based input while ensuring low energy consumption. As a result, we build several classifiers for a variety of use cases (e.g. single or double taps with the dominant or non-dominant hand). We show that a subset of just 5 features provides high discrimination power and results in high recognition accuracy. We also make our software publicly available, so that others can build upon our work.