Inferring Identity Using Accelerometers in Television Remote Controls

Keng-hao Chang, Jeffrey Hightower, Branislav Kveton
Pervasive Computing (2009)

Abstract

We show that accelerometers embedded in a television remote control can be used to distinguish household members based on the unique way each person wields the remote. This personalization capability can be applied to enhance digital video recorders with show recommendations per family-member instead of per device or as an enabling technology for targeted advertising. Based on five 1-3 week data sets collected from real homes, using 372 features including key press codes, key press timing, and 3-axis acceleration parameters including dominant frequency, energy, mean, and variance, we show household member identification accuracy of 70-92% with a Max-Margin Markov Network (M3 N) classifier.