Power and accuracy trade-offs in sound-based context recognition systems

Abstract

This paper presents an empirical design methodology to optimize a context recognition system with respect to a trade-off between power consumption and recognition performance rather than straightforward maximization of the recognition rate. As illustration, we present a case study in which the interaction with different household appliances is detected by means of a wrist worn microphone and accelerometers. This example, which is embedded in the larger context of an assisted living scenario, demonstrates that the proposed method leads to improvements in battery lifetime by a factor of 2–4 with only little degradation in recognition performance. For a specific sensor node, we show that a recognition rate of 94% can be achieved with a power consumption of just 3.3 mW, resulting in a battery lifetime of 168 h.