Energy-efficient mobile gesture recognition with computation offloading

Gesture recognition is a novel and compelling user input modality which allows users to interact quickly and naturally with their devices with less demand on their visual attention. Continuous gesture recognition places stringent demands on device power consumption, battery life and processing capability. In this work, we show that we can reduce the energy consumed during continuous gesture recognition on a mobile device with the delegation of the pre-processing stages, which filter out non-gesture segments, to a low power node that is separate from the main CPU. The main CPU can thus be kept in stop mode until a potential gesture is detected by the low power node, invoking the main processor to perform the computation-intensive gesture classification to detect which exact gesture has been performed by the user. We present details of the processing performance and power consumed at each step of the processing pipeline, showing the extent of power savings achieved. Experiments were conducted for detailed evaluation of the power consumption of the optimized gesture pipeline.