Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array

TLDR

Context-aware computing enables a wearable or mobile device to adapt its behavior based on the user’s state and surroundings. The study designs, implements, and evaluates a wearable system that learns context‑dependent personal preferences by identifying user states and observing interactions in those states. The system learns online without external supervision using machine‑learning and statistical techniques, and a case study demonstrates its integration into a context‑aware mobile phone. The method successfully creates a meaningful user context model using only data from comfortable wearable sensor devices.

Abstract

Context-aware computing describes the situation where a wearable/mobile computer is aware of its user's state and surroundings and modifies its behavior based on this information. We designed, implemented, and evaluated a wearable system which can learn context-dependent personal preferences by identifying individual user states and observing how the user interacts with the system in these states. This learning occurs online and does not require external supervision. The system relies on techniques from machine learning and statistical analysis. A case study integrates the approach in a context-aware mobile phone. The results indicate that the method is able to create a meaningful user context model while only requiring data from comfortable wearable sensor devices.