Sensor Fusion Using Dempster-Shafer Theory

TLDR

Context‑sensing for context‑aware HCI challenges traditional sensor fusion by requiring dynamic sensor configuration and measurement, and the Dempster‑Shafer theory offers uncertainty management and inference mechanisms analogous to human reasoning. The Sensor Fusion for Context‑aware Computing Project aims to build a generalizable, systematic sensor‑fusion architecture. The authors implemented a Dempster‑Shafer fusion algorithm, compared it to Bayesian and weighted‑sum probability methods, and evaluated it by tracking users’ focus of attention from multiple cues. Experiments produced promising, thought‑provoking results that encourage further research.

Abstract

Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Context-aware Computing Project aims to build a generalizable sensor fusion architecture in a systematic way. This naturally leads us to choose the Dempster-Shafer approach as our first sensor fusion implementation algorithm. This paper discusses the relationship between Dempster-Shafer theory and the classical Bayesian method, describes our sensor fusion research work using Dempster-Shafer theory in comparison with the weighted sum of probability method. The experimental approach is to track a user’s focus of attention from multiple cues. Our experiments show promising, thought-provoking results encouraging further research.