Bayesian recognition of motion related activities with inertial sensors

This work presents the design and evaluation of an activity recognition system for seven important motion related activities. The only sensor used is an Inertial Measurement Unit (IMU) worn on the belt. For classification, we applied Bayesian techniques, based on relevant features of the IMU raw data which are calculated in real time. Based on a complete labelled data set, i.e. supervised by an observing human judge, a K2 learning algorithm by Cooper and Herskovits was used to construct the Bayesian Network (BN) of the features. Our comparison of dynamic and static inference algorithms, based on the evaluation of the labelled data sets recorded from 16 male and female subjects show that a Hidden Markov Model (HMM) based on a learnt BN provides the best results. Author Keywords Activity Recognition, Context Inference, Bayesian Networks, Inertial Navigation. ACM Classification Keywords I.2.3 Deduction and Theorem Proving - Deduchtion, Inference engines, Nonmonotonic reasoning and belief...

• Type: Conference
• Year: 2010
• Where: HUC
• Authors: Korbinian Frank, Maria Josefa Vera Nadales, Patrick Robertson, Tom Pfeifer
• Keywords: Activity Recognition, Bayesian Networks, HUC 2010, Human Computer Interaction, Labelled Data