By Michelle Hampson

By sensing human activity and adjusting the environmental settings accordingly, smart-home systems could help create more energy-efficient and sustainable buildings. However, there have been privacy concerns when it comes to these systems monitoring peoples’ activity, and smart-home systems can require heavy amounts of data crunching to learn how to respond to a given environment.

A new smart system, dubbed Chameleon, is designed to address both of these issues. It was recently tested in two different environments over the course of a month, and could predict human activity with 87 to 99 percent accuracy after just one week of training. The results are described in a study published 6 April in IEEE Internet of Things Journal.