Don't slow me down: Bringing energy efficiency to continuous gesture recognition

Gesture is a compelling user interaction modality for enabling truly on-the-go interactions. Unlike keyboard and touch screen interactions which require considerable visual attention and impose stringent constrains on the form factor of mobile devices, people can easily use hand gestures to perform simple actions (e.g. retrieve voice mail) without having to slow down. In this paper we present an efficient gesture recognition pipeline optimized for "continuous" recognition while minimizing processing overhead and enhancing usability by not requiring the user to delimit explicitly the start and end of gestures. The pipeline is constructed to allow for early filtering of unwanted sensor data with minimal processing cost, and limiting the invocation of processing intensive stages (i.e. HMM) to a limited subset of data (<; 5% of sensor data). We also present our evaluation results from a 10 user experiment using 17 gestures and demonstrate that we can achieve considerable processing and power saving without impacting overall recognition accuracy.