Comparison of exact static and dynamic Bayesian context inference methods for activity recognition
Korbinian Frank, Matthias Röckl, María Josefa Vera Nadales, Patrick Robertson, Tom A. Pfeifer · 2010
This paper compares the performance of inference in static and dynamic Bayesian Networks. For the comparison both kinds of Bayesian networks are created for the exemplary application activity recognition. Probability and structure of the Bayesian Networks have been learnt automatically from a recorded data set consisting of acceleration data observed from an inertial measurement unit. Whereas dynamic networks incorporate temporal dependencies which affect the quality of the activity recognition, inference is less complex for dynamic networks. As performance indicators recall, precision and processing time of the activity recognition are studied in detail. The results show that dynamic Bayesian Networks provide considerably higher quality in the recognition but entail longer processing times.