Adaptive Sampling and Duty Cycling for Smartphone Accelerometer

Mobile device based sensing provides a vast variety of user contextual information. To continuously acquire, classify and reason any context through built-in sensors cause mobile device batteries to deplete rapidly. Therefore, an urgent need has arisen to apply power saving approaches at the low level sensor operations while inferring user relevant context. In this paper, an adaptive sensing strategy is proposed to fill the mentioned deficiency within the realm of Human Activity Recognition (HAR). The strategy aims at inferring any user relevant postural activities through smartphone accelerometer by providing two intuitive power saving methods, which assign adaptive pairs of sampling frequencies and duty cycles during sensor operations. The proposed methods show from 20% up to 50% efficiency enhancement in power consumption caused by the accelerometer with respect to overall 15% accuracy decrease in context inference depending on the degree of user activity dynamics.