  Emerging wearable sensors have enabled the unprecedented ability to
continuously monitor human activities for healthcare purposes. However, with so
many ambient sensors collecting different measurements, it becomes important
not only to maintain good monitoring accuracy, but also low power consumption
to ensure sustainable monitoring. This power-efficient sensing scheme can be
achieved by deciding which group of sensors to use at a given time, requiring
an accurate characterization of the trade-off between sensor energy usage and
the uncertainty in ignoring certain sensor signals while monitoring. To address
this challenge in the context of activity monitoring, we have designed an
adaptive activity monitoring framework. We first propose a switching Gaussian
process to model the observed sensor signals emitting from the underlying
activity states. To efficiently compute the Gaussian process model likelihood
and quantify the context prediction uncertainty, we propose a block circulant
embedding technique and use Fast Fourier Transforms (FFT) for inference. By
computing the Bayesian loss function tailored to switching Gaussian processes,
an adaptive monitoring procedure is developed to select features from available
sensors that optimize the trade-off between sensor power consumption and the
prediction performance quantified by state prediction entropy. We demonstrate
the effectiveness of our framework on the popular benchmark of UCI Human
Activity Recognition using Smartphones.
