Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

Understand

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.